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Abstract Global change is influencing production and respiration in ecosystems across the globe. Lakes in particular are changing in response to climatic variability and cultural eutrophication, resulting in changes in ecosystem metabolism. Although the primary drivers of production and respiration such as the availability of nutrients, light, and carbon are well known, heterogeneity in hydrologic setting (for example, hydrological connectivity, morphometry, and residence) across and within regions may lead to highly variable responses to the same drivers of change, complicating our efforts to predict these responses. We explored how differences in hydrologic setting among lakes influenced spatial and inter annual variability in ecosystem metabolism, using high-frequency oxygen sensor data from 11 lakes over 8 years. Trends in mean metabolic rates of lakes generally followed gradients of nutrient and carbon concentrations, which were lowest in seepage lakes, followed by drainage lakes, and higher in bog lakes. We found that while ecosystem respiration (ER) was consistently higher in wet years in all hydrologic settings, gross primary production (GPP) only increased in tandem in drainage lakes. However, interannual rates of ER and GPP were relatively stable in drainage lakes, in contrast to seepage and bog lakes which had coefficients of variation in metabolism between 22–32%. We explored how the geospatial context of lakes, including hydrologic residence time, watershed area to lake area, and landscape position influenced the sensitivity of individual lake responses to climatic variation. We propose a conceptual framework to help steer future investigations of how hydrologic setting mediates the response of metabolism to climatic variability. 

Abstract Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. In lakes, pelagic gross primary productivity (GPP) is strongly controlled by inputs of nutrients and dissolved organic matter. Although past studies have developed process models of this nutrient‐color paradigm (NCP), broad empirical tests of these models are scarce. We used data from 58 globally distributed, mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. The model includes three state variables–dissolved phosphorus, terrestrial dissolved organic carbon (DOC), and phytoplankton biomass–and generates realistic predictions for equilibrium rates of pelagic GPP. We calibrated our model using a Bayesian data assimilation technique on a subset of lakes where DOC and total phosphorus (TP) loads were known. We then asked how well the calibrated model performed with a larger set of lakes. Revised parameter estimates from the updated model aligned well with existing literature values. Observed GPP varied nonlinearly with both inflow DOC and TP concentrations in a manner consistent with increasing light limitation as DOC inputs increased and decreasing nutrient limitation as TP inputs increased. Furthermore, across these diverse lake ecosystems, model predictions of GPP were highly correlated with observed values derived from high‐frequency sensor data. The GPP predictions using the updated parameters improved upon previous estimates, expanding the utility of a process model with simplified assumptions for water column mixing. Our analysis provides a model structure that may be broadly useful for understanding current and future patterns in lake primary production.