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Abstract Global change may contribute to ecological changes in high-elevation lakes and reservoirs, but a lack of data makes it difficult to evaluate spatiotemporal patterns. Remote sensing imagery can provide more complete records to evaluate whether consistent changes across a broad geographic region are occurring. We used Landsat surface reflectance data to evaluate spatial patterns of contemporary lake color (2010–2020) in 940 lakes in the U.S. Rocky Mountains, a historically understudied area for lake water quality. Intuitively, we found that most of the lakes in the region are blue (66%) and were found in steep-sided watersheds (>22.5°) or alternatively were relatively deep (>4.5 m) with mean annual air temperature (MAAT) <4.5°C. Most green/brown lakes were found in relatively shallow sloped watersheds with MAAT ⩾4.5°C. We extended the analysis of contemporary lake color to evaluate changes in color from 1984 to 2020 for a subset of lakes with the most complete time series ( n = 527). We found limited evidence of lakes shifting from blue to green states, but rather, 55% of the lakes had no trend in lake color. Surprisingly, where lake color was changing, 32% of lakes were trending toward bluer wavelengths, and only 13% shifted toward greener wavelengths. Lakes and reservoirs with the most substantial shifts toward blue wavelengths tended to be in urbanized, human population centers at relatively lower elevations. In contrast, lakes that shifted to greener wavelengths did not relate clearly to any lake or landscape features that we evaluated, though declining winter precipitation and warming summer and fall temperatures may play a role in some systems. Collectively, these results suggest that the interactions between local landscape factors and broader climatic changes can result in heterogeneous, context-dependent changes in lake color. 

Abstract For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science. 

Artisanal and small-scale gold mining (ASGM) is the largest global source of anthropogenic mercury emissions. However, little is known about how effectively mercury released from ASGM is converted into the bioavailable form of methylmercury in ASGM-altered landscapes. Through examination of ASGM-impacted river basins in Peru, we show that lake area in heavily mined watersheds has increased by 670% between 1985 and 2018 and that lakes in this area convert mercury into methylmercury at net rates five to seven times greater than rivers. These results suggest that synergistic increases in lake area and mercury loading associated with ASGM are substantially increasing exposure risk for people and wildlife. Similarly, marked increases in lake area in other ASGM hot spots suggest that “hydroscape” (hydrological landscape) alteration is an important and previously unrecognized component of mercury risk from ASGM. 

Abstract In land surface models (LSMs), the hydraulic properties of the subsurface are commonly estimated according to the texture of soils at the Earth's surface. This approach ignores macropores, fracture flow, heterogeneity, and the effects of variable distribution of water in the subsurface oneffectivewatershed‐scale hydraulic variables. Using hydrograph recession analysis, we empirically constrain estimates of watershed‐scale effective hydraulic conductivities (K) and effective drainable aquifer storages (S) of all reference watersheds in the conterminous United States for which sufficient streamflow data are available (n = 1,561). Then, we use machine learning methods to model these properties across the entire conterminous United States. Model validation results in high confidence for estimates of log(K) (r² > 0.89; 1% < bias < 9%) and reasonable confidence forS(r² > 0.83; −70% < bias < −18%). Our estimates of effectiveKare, on average, two orders of magnitude higher than comparable soil‐texture‐based estimates of averageK, confirming the importance of soil structure and preferential flow pathways at the watershed scale. Our estimates of effectiveScompare favorably with recent global estimates of mobile groundwater and are spatially heterogeneous (5–3,355 mm). Because estimates ofSare much lower than the global maximums generally used in LSMs (e.g., 5,000 mm in Noah‐MP), they may serve both to limit model spin‐up time and to constrain model parameters to more realistic values. These results represent the first attempt to constrain estimates of watershed‐scale effective hydraulic variables that are necessary for the implementation of LSMs for the entire conterminous United States. 

Abstract Rivers are among the most imperiled ecosystems globally, yet we do not have broad‐scale understanding of their changing ecology because most are rarely sampled. Water color, as perceived by the human eye, is an integrative measure of water quality directly observed by satellites. We examined patterns in river color between 1984 and 2018 by building a remote sensing database of surface reflectance, RiverSR, extracted from 234,727 Landsat images covering 108,000 kilometers of rivers > 60 m wide in the contiguous USA. We found 1) broad regional patterns in river color, with 56% of observations dominantly yellow and 38% dominantly green; 2) river color has three distinct seasonal patterns that were synchronous with flow regimes; 3) one third of rivers had significant color shifts over the last 35 years. RiverSR provides the first map of river color and new insights into macrosystems ecology of rivers.