Model‐Data Fusion to Test Hypothesized Drivers of Lake Carbon Cycling Reveals Importance of Physical Controls

Abstract

Formal integration of models and data to test hypotheses about the processes controlling carbon dynamics in lakes is rare, despite the importance of lakes in the carbon cycle. We built a suite of models (n = 102) representing different hypotheses about lake carbon processing, fit these models to data from a north‐temperate lake using data assimilation, and identified which processes were essential for adequately describing the observations. The hypotheses that we tested concerned organic matter lability and its variability through time, temperature dependence of biological decay, photooxidation, microbial dynamics, and vertical transport of water via hypolimnetic entrainment and inflowing density currents. The data included epilimnetic and hypolimnetic CO2 and dissolved organic carbon, hydrologic fluxes, carbon loads, gross primary production, temperature, and light conditions at high frequency for one calibration and one validation year. The best models explained 76–81% and 64–67% of the variability in observed epilimnetic CO2 and dissolved organic carbon content in the validation data. Accurately describing C dynamics required accounting for hypolimnetic entrainment and inflowing density currents, in addition to accounting for biological transformations. In contrast, neither photooxidation nor variable organic matter lability improved model performance. The temperature dependence of biological decay (Q10) was estimated at 1.45, significantly lower than the commonly assumed Q10 of 2. By confronting multiple models of lake C dynamics with observations, we identified processes essential for describing C dynamics in a temperate lake at daily to annual scales, while also providing a methodological roadmap for using data assimilation to further improve understanding of lake C cycling.

Metabolism in a deep hypertrophic aquatic ecosystem with high water-level fluctuations: a decade of records confirms sustained net heterotrophy

“…Long-term monitoring of whole ecosystems can help identify variations related to their environmental drivers, because they are likely to include significant variations of these drivers without the scaling problem of experimental assessments. Among these drivers, physical features are the least studied in relation to their effects on metabolism, in part due to the complexity of disentangling the simultaneous effects of multiple drivers ( Hoellein, Bruesewitz & Richardson, 2013 ; Coloso, Cole & Pace, 2011 ; Hararuk et al, 2018 ). Nutrients are among the main drivers of primary production in oligotrophic and mesotrophic conditions, hindering the identification of the effects of physical features.…”

“…Nutrients are among the main drivers of primary production in oligotrophic and mesotrophic conditions, hindering the identification of the effects of physical features. Because of this, in eutrophic and hypertrophic systems, where nutrient availability is not limiting, the effects of physical drivers may be easier to isolate and quantify (e.g., Coloso, Cole & Pace, 2011 ; Hararuk et al, 2018 ).…”

Improving estimates and forecasts of lake carbon dynamics using data assimilation

“…Indeed, sometimes it is difficult to distinguish which processes are important for accurately predicting system dynamics due to equifinality in estimated parameters or insensitive model structures. For example, Hararuk et al () found it difficult to distinguish between biodegradation and photodegradation rates as these rates were additive and drivers for each process were positively correlated. In such cases, assimilating additional sources of information to constrain processes or structure can help; for example, in situ incubation experiments could be conducted to help constrain rates of photodegradation and biodegradation.…”

“…Although we include many essential processes for describing lake C dynamics when assimilating real observations, we did not explicitly test different model structures (other than including carbonate equilibria dynamics, see Supporting Information), which is another utility of data assimilation. For an example of a robust model structure comparison (102 different models) using data assimilation with the same lake dataset used in this analysis, see Hararuk et al (). While our model structure performed well when assimilating real observations from East Long Lake, it is unclear if the same model structures used here and in Hararuk et al () will perform equally well across different lakes.…”

“…For an example of a robust model structure comparison (102 different models) using data assimilation with the same lake dataset used in this analysis, see Hararuk et al (). While our model structure performed well when assimilating real observations from East Long Lake, it is unclear if the same model structures used here and in Hararuk et al () will perform equally well across different lakes. For example, including carbonate equilibria processes did not affect our estimates of the DOC and CO 2 pools (Supporting Information); however, applying a model without explicit consideration of carbonate equilibria processes to hardwater lakes and lakes with different pH dynamics than East Long Lake could be quite problematic.…”