In volatile environments, humans and animals face different forms of uncertainty to which they must adapt to thrive. However, our understanding of the neural basis of this adaptation is incomplete, despite, for instance, long-standing arguments about its possible dependence on neuromodulation. Here, we take advantage of the well-known spatial remapping of hippocampal place cells in the face of environmental change to interrogate these processes. We performed calcium imaging in CA1 place cells using a new Uncertain Reward virtual reality Task (URTask), in which mice run along a linear track and lick for a water reward. The reward location could be largely stable, indicating low uncertainty, or substantially variable, a form of expected uncertainty. We found that expected uncertainty enhanced the prevalence of cells that tracked the reward on a trial-by-trial basis. Additionally, the reward and track end anchored a warped spatial metric in which the distance between the two was normalized. Once the mice had been familiarized to low or high expected uncertainty, without warning, we translated the reward location to a narrow zone far from predictive bounds, thus inducing unexpected uncertainty. Unexpected uncertainty caused remapping of place cells, with no difference in the overall remapping proportion in the spatial, external, reference frame based on prior uncertainty experience. Instead, starting from a state of high, versus low, expected uncertainty increased the proportion of reward place cells that moved to follow the reward after the unexpected change in reward location, a condition that we termed uncertainty interaction. By contrast, starting from a state of low uncertainty led to persistence of the location of the initial reward. We also observed that the flexible warped sequence induced by experience in expected uncertainty flexibly translated to the new reward location. Hence, by inducing different forms of uncertainty in reward location and looking at their interaction, we show that uncertainty generates remapping in parallel, task-relevant, reference frames.