A human circulatory system is composed of more than 50,000 miles of blood vessels. Such a huge network of vessels is responsible for the elevated pressure values within large arteries. As such, modeling of large blood arteries requires a full modeling of circulatory system. This in turn is computationally not affordable. Thus, a multiscale modeling of the arterial network is a necessity. The multiscale approach is achieved through, first, modeling the arterial regions of interst with 3D models and the rest of the circulatory network with reduced-dimensional (reduced-D) models, then coupling the multiscale domains together. Though reduced-D models can well reproduce physiology, calibrating them to fit 3D patient-specific Fluid Structure Interaction (FSI) geometries has received little attention. For this reason, this work develops calibration methods for reduced-D models using adjoint based methods. We also propose a reduced modeling complexcity (RMC) approach that reduces the calibration cost of expensive FSI models using pure fluid modeling. Finally, all of the developed calibration techniques are tested on patient-specific arterial geometries, showing the power and stability of the proposed calibration methods.