Currently, many water quality variables can be automatically monitored at subdaily frequencies, but most environmental datasets still rely heavily on comparatively low-frequency (e.g., monthly) sampling campaigns. Taking advantage of the long-term ecological data available from polymictic Lake Müggelsee (Germany) at weekly temporal scales, we tested whether dynamic factor analysis (DFA), a dimension-reduction technique especially designed for time series with data gaps, was able to reproduce the same underlying patterns if the resolution of the observations was artificially reduced from weekly to once every 2 or 4 weeks (2-weekly or 4-weekly, respectively) and whether the same results were obtained for different variables (water temperature, water chemistry, and plankton). As expected, the reduction in data resolution and the addition of gaps increased the amount of variance in our case studies; however, water temperature patterns, which generally vary only slowly if considered at broader temporal scales, were represented well in the 4-weekly data model. By contrast, total algal biomass and nutrient dynamics, which usually fluctuate more rapidly, were not. We further assessed the representativeness of spot samples taken by selection at 4-weekly and seasonal intervals compared to samples obtained by averaging the weekly data over these 2 intervals. In this case, the resulting observation variance was more pronounced in DFA models of the samples taken by selection than of those obtained by averaging. The introduction of additional artificial gaps in one univariate and one multivariate model decreased the amount of information that could be explained by the pattern(s) extracted by DFA. Overall, we found that a too low sampling frequency could be a limiting factor when studying interactions and responses to changing conditions in aquatic systems, particularly when biological variables are involved.