Eutrophication represents a major threat to freshwater systems and climate change is expected to drive further increases in freshwater primary productivity. However, long-term in-situ data is available for very few lakes and makes identifying trends and drivers of eutrophication challenging. Using remote sensing data, we conducted a retrospective analysis of long-term trends in trophic status across the Intermountain West, a region with understudied water quality trends and limited long-term datasets. We found that most lakes (55%) were not exhibiting shifts in trophic status from 1984-2019. Our results also show that increases in eutrophication were rare (3% of lakes) during this period, and that lakes exhibiting negative trends in trophic status were more common (17% of lakes). Lakes that were not trending occupied a wide range of lake and landscape characteristics, whereas lakes that were becoming less eutrophic tended to be in more heavily developed catchments. Our results highlight that while there are well-established narratives that climate change can lead to more eutrophication of lakes, this is not broadly observed in our dataset, with more lakes becoming more oligotrophic than lakes becoming eutrophic.

Rivers are an important source of freshwater that support societal needs and natural ecosystems, functioning as both collectors for watersheds and distributors along river corridors. Human-made infrastructure (dams, roads, canals) of various kinds have been built on and along rivers to access drinking water, generate energy, mitigate floods, and support industrial and agricultural production. However, due to the long and inconsistent history of constructing and recording these structures, we lack a globally consistent knowledge about where different types of infrastructure are. Here, we used a simple yet consistent method to visually locate and classify different infrastructures that could act as obstructions on rivers that are wider than 30 meters (total length ~2.1 million km globally). Our approach is based on Google Maps’ high resolution satellite images, which for many places have meter-scale resolution. We recently completed global-scale mapping and classifying different obstructions, and are conducting quality checks. In total, we identified ≥ 40,000 unique obstructions, including large dams and smaller weirs, control structures, partial barriers, as well as low-head dams that are often not included in other databases. This Global River Obstruction Dataset, or GROD, once fully validated, will be freely available to the public. We anticipate that it will be of wide interest to hydrological modeling, aquatic ecosystem, geomorphology, and water resource management communities.

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.

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥ 30m) globally. We based our mapping on Google Earth Engine’s high resolution images from 2018–2020, which for many places have meter-scale resolution. The resulting dataset, the Global River Obstruction Database (GROD), consists of 29,877 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured 90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities.