Self-reconfiguration enables a dynamically adaptive system (DAS) to satisfy requirements even as detrimental system and environmental conditions arise. A DAS, especially one intertwined with physical elements, must increasingly reason about and cope with unpredictable events in its execution environment. Unfortunately, it is often infeasible for a human to exhaustively explore, anticipate, or resolve all possible system and environmental conditions that a DAS will encounter as it executes. While uncertainty can be difficult to define, its effects can hinder the adaptation capabilities of a DAS. The concept of uncertainty has been extensively explored by other scientific disciplines, such as economics, physics, and psychology. As such, the software engineering DAS community can benefit from leveraging, reusing, and refining such knowledge for developing a DAS. By synthesizing uncertainty concepts from other disciplines, this paper revisits the concept of uncertainty from the perspective of a DAS, proposes a taxonomy of potential sources of uncertainty at the requirements, design, and execution phases, and identifies existing techniques for mitigating specific types of uncertainty. This paper also introduces a template for describing different types of uncertainty, including fields such as source, occurrence, impact, and mitigating strategies. We use this template to describe each type of uncertainty and illustrate the uncertainty source in terms of an example DAS application from the intelligent vehicle systems (IVS) domain.