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Abstract Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream–lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake’s epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2–2.5 mg O 2 L −1  day −1 (9–670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream–lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulatedmore »littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors.« less

Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.

Abstract

This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.

Abstract. The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic–ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing.more »To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50–60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.« less