In this first worldwide synthesis of in situ and satellite‐derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade−1) between 1985 and 2009. Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors—from seasonally ice‐covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade−1) to ice‐free lakes experiencing increases in air temperature and solar radiation (0.53°C decade−1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes.

Cyanobacteria are predicted to increase due to climate and land use change. However, the relative importance and interaction of warming temperatures and increased nutrient availability in determining cyanobacterial blooms are unknown. We investigated the contribution of these two factors in promoting phytoplankton and cyanobacterial biovolume in freshwater lakes. Specifically, we asked: (1) Which of these two drivers, temperature or nutrients, is a better predictor of cyanobacterial biovolume? (2) Do nutrients and temperature significantly interact to affect phytoplankton and cyanobacteria, and if so, is the interaction synergistic? and (3) Does the interaction between these factors explain more of the variance in cyanobacterial biovolume than each factor alone? We analyzed data from . 1000 U.S. lakes and demonstrate that in most cases, the interaction of temperature and nutrients was not synergistic; rather, nutrients predominantly controlled cyanobacterial biovolume. Interestingly, the relative importance of these two factors and their interaction was dependent on lake trophic state and cyanobacterial taxon. Nutrients played a larger role in oligotrophic lakes, while temperature was more important in mesotrophic lakes: Only eutrophic and hyper-eutrophic lakes exhibited a significant interaction between nutrients and temperature. Likewise, some taxa, such as Anabaena, were more sensitive to nutrients, while others, such as Microcystis, were more sensitive to temperature. We compared our results with an extensive literature review and found that they were generally supported by previous studies. As lakes become more eutrophic, cyanobacteria will be more sensitive to the interaction of nutrients and temperature, but ultimately nutrients are the more important predictor of cyanobacterial biovolume.

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985–2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

An experiment in >1000 river and riparian sites found spatial patterns and controls of carbon processing at the global scale.

Highlights  The General Lake Model (GLM) is stress tested against 32 globally distributed lakes.  There was low correlation between input data uncertainty and model performance.  Model performance related to lake-morphometry, light extinction and flow regime; deep, clear lakes with high residence times had the lowest model error.