Modeling historical trends in Lake Superior total nitrogen concentrations
Introduction
Nitrate concentrations in Lake Superior increased approximately fivefold over the past century, resulting in a severe stoichiometric imbalance in the lake (Sterner et al., 2007). The upward trend in nitrate concentrations was first documented by Weiler (1978), and was examined in more detail by Bennett (1986) and more recently by (Sterner et al., 2007). Atmospheric emissions of NOx increased tenfold over the same period (U.S.EPA, 2000); atmospheric nitrate deposition was previously thought to fully explain the rise in lake nitrate concentrations (Bennett, 1986). A stable nitrogen isotope study revealed very low δ15N-NO3 values, consistent with the hypothesis that atmospheric N inputs are directly responsible for rising nitrate concentration in the lake (Ostrom et al., 1998). Recent studies, however, have questioned whether incomplete biological assimilation of atmospherically-deposited N can account for the observed rise in nitrate concentrations (Sterner et al., 2007, Finlay et al., 2007). Stable oxygen isotope ratios of in-lake nitrate (δ18O-NO3−) indicate that the majority of nitrate in the lake has undergone in-situ oxidation (Finlay et al., 2007), suggesting that nitrogen inputs to the lake are assimilated quickly.
Studies of nutrient uptake and primary productivity confirm that nitrogen cycles through the biological pool in Lake Superior much more quickly than it is added to or removed from the system. In-situ measurements of ammonium and nitrate uptake suggest that uptake of both inorganic forms of nitrogen exceeds inputs to the lake on an annual scale (Kumar et al., 2008). Recent estimates of primary production (Urban et al., 2005) also indicate that annual biological uptake of nitrogen exceeds the annual supply to the lake, as do measurements of seasonal nitrate drawdown in the water column (Urban, 2009).
Rates of nitrification and denitrification are less well known. Because Lake Superior is an ultraoligotrophic system, the flux of labile organic matter to the sediments is low, and the majority of sediment metabolism in the lake is oxic (Kumar et al., 2008, Carlton et al., 1989). Nitrification in and subsequent nitrate efflux from lake sediments has been observed (Carlton et al., 1989, Heinen & McManus, 2004), whereas the few published measurements of denitrification indicate that it is a minor process (Carlton et al., 1989). Sediment burial of nitrogen has not been well quantified.
Many questions surrounding the historical increase in nitrate concentrations in Lake Superior remain unanswered. It is unclear whether the lake is already responding to reductions in NOx emissions to the atmosphere or how quickly such a response will occur. Little is known about the effect historical changes in the watershed may have had on terrigenous N inputs to the lake, or if biological cycling of nitrogen in the lake has varied over time. Chapra et al. (2009) employed a mass-balance model to perform an inverse analysis of chloride loading to the Great Lakes; in this paper, we take a similar approach to reconstructing historical N loading and losses in Lake Superior. Dependence on scarce data produces a high level of uncertainty when performing this type of inverse calculation, particularly when dealing with nonconservative substances such as nutrients (Chapra et al., 2009). We model total nitrogen rather than individual species to minimize the number of assumptions made about transformation processes internal to the lake. The model is used to refine our understanding of historical nitrogen sources and sinks and current rates of in-lake nitrogen cycling processes. We then explore several input/ouput conditions that may have produced the observed historical record of nitrate in the lake. Finally, we forecast nitrogen concentrations in Lake Superior over the next century.
Section snippets
Model development
We constructed a simple mass-balance model to explore total nitrogen dynamics in Lake Superior (Fig. 1), governed by the following ordinary differential equation:where CN is the concentration of total nitrogen (N) in the lake, Watm(t) is atmospheric loading in moles year−1, Wtrib(t) is tributary loading in moles year−1, Qout is the flow rate through the St. Mary's River (L year−1), Δ(t) is an optional estimated loading/burial term (mol year−1), kloss is the net
Results
Assuming a constant net loss rate, kloss, of 3.21 × 109 mol year−1 (based on Urban et al. 2005) results in hindcasted total N concentrations substantially lower than the adjusted observations (Fig. 4a). Using a rate of 1.33 × 109 mol year−1 (based on Heinen and McManus 2004), however, results in a rough fit to adjusted observations, though the residuals are clearly not evenly distributed (Fig. 4a). The modeled total N concentrations do not rise as high or as quickly as observations between 1940 and
Discussion
Given a sufficiently low constant net N loss rate of around 1.33 × 109 mol year−1 and our estimated historical loading, the model reproduces the observed historical trend and present-day N concentrations in the lake (Fig. 4a). While this loss rate is lower than the estimated 1900 steady-state loss rate based on our model inputs and constraints, a great deal of uncertainty surrounds model output for 1900. Calculation of a steady-state loss term requires the steady-state lake concentration and loading
Conclusions
Our results suggest that several factors may have played a role in producing the observed rapid rise in nitrate (and total nitrogen) in Lake Superior. Increased atmospheric deposition in response to NOx emissions and a historical increase in river inflows were the major factors causing increased NO3− concentrations in the lake. Mid-century loading may have been higher than assumed in our model, due either to (1) a nonlinear relationship between national atmospheric NOx emissions and atmospheric
Acknowledgments
The authors would like to thank R. Hecky and two reviewers for their helpful comments on early versions of this manuscript. Unpublished nitrate data were provided by Environment Canada. This work was supported by the National Science Foundation under grants DGE-0333401, OCE-0628545, and DUE-0806569.
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