Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes, such as primary productivity, are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of in situ observations that are available. Limited landscape level observations means we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing dataset, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high discharge and low variation in their seasonal extent are generally more stable while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our research reveals previously unexamined spatiotemporal patterns in lake seasonality and demonstrates the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to systematically examine changing lotic ecosystems at a national scale.

Lakes are traditionally classified based on their thermal regime and trophic status. While this classification adequately captures many lakes, it is not sufficient to understand seasonally ice-covered lakes, the most common lake type on Earth. Here, we propose an additional classification to differentiate under-ice stratification. When ice forms in smaller and deeper lakes, inverse stratification will form with a thin buoyant layer of cold water (near 0oC) below the ice, which remains above a deeper 4oC layer. In contrast, the entire water column can cool to ~0oC in larger and shallower lakes. We suggest these alternative conditions for dimictic lakes be termed “cryostratified” and “cryomictic.” We describe the inverse thermal stratification in 19 highly varying lakes and derive a model that predicts the temperature profile as a function of wind stress, area, and depth. The model opens up for a more precise prediction of lake responses to a warming climate.