We present a Distributionally Robust Optimization (DRO) mathematical framework for addressing the Transmission Expansion Planning (TEP) problem that accounts for long-and short-term uncertainty for significant penetration of new renewable distributed energy resources (DER). We consider the case where the exact probability distribution of future DER is ambiguous, i.e., difficult to accurately estimate. The resulting DRO model is robust against all possible probability distribution functions that lie on an ambiguity set with partial information of the exact probability distribution. The proposed framework allows finding an optimal transmission expansion plan without relying on strong assumptions about exact probability distribution. Additionally, our proposed approach enables transmission planners to consider, within a DRO framework, the effect on the structure of short-term DER uncertainties due to the conditional information of different scenarios for the long-term trends. We illustrate the effectiveness of the proposed approach on the IEEE IIS-bus test system.