Nonstationary disordered materials and media, those for which the probability distribution function of any property varies spatially when shifted in space, are abundant and encountered in astrophysics, oceanography, air pollution patterns, large-scale porous media, biological tissues and organs, and composite materials. Their reconstruction and modeling is a notoriously difficult and largely unsolved problem. We propose a method for reconstructing a broad class of such media based on partitioning them into locally stationary zones. Two methods are used for the partitioning. One is based on the Shannon entropy, while the second method utilizes a watershed transform. The locally stationary zones are then reconstructed based on a cross-correlation function and one-dimensional raster path that we recently introduced [P. Tahmasebi and M. Sahimi, Phys. Rev. Lett. 110, 078002 (2013)], with overlaps between the zones to ensure seamless transition from one zone to another. A large number of examples, including porous media, ecological systems, disordered materials, and biological tissues and organs, are reconstructed and analyzed to demonstrate the accuracy of the method.