Improving estimates and forecasts of lake carbon dynamics using data assimilation

Abstract

Lakes are biogeochemical hotspots on the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. Observations and models of lake carbon pools and fluxes are rarely explicitly combined through data assimilation despite successful use of this technique in other fields. Data assimilation adds value to both observations and models by constraining models with observations of the system and by leveraging knowledge of the system formalized by the model to objectively fill observation gaps. In this article, we highlight the utility of data assimilation in lake carbon cycling research by using the ensemble Kalman filter to combine simple lake carbon models with observations of lake carbon pools and fluxes. We demonstrate that data assimilation helps reduce uncertainty in estimates of lake carbon pools and fluxes and more accurately estimate the true carbon pool size compared to estimates derived from observations alone. Data assimilation techniques should be embraced as valuable tools for lake biogeochemists interested in learning about ecosystem dynamics and forecasting ecosystem states and processes. Lakes are areas of intense carbon (C) processing. Current estimates of C exported annually from terrestrial ecosystems to inland waters are on par with annual global land net ecosystem production (Randerson et al. 2002; Drake et al. 2017). Nearly 50% of this C is transferred to the atmosphere and about 20% is buried, forming a sediment pool that is now larger than the remainder of the terrestrial biosphere (e.g., land plants and soils; Tranvik et al. 2009; Cole 2013). Clearly, lakes play an integral role in global C cycling, and it is important to understand the drivers of magnitudes and variability of C pools and fluxes. Observations of lake C pools and fluxes have fundamentally advanced our understanding of lake C cycling. For example, Cole et al. (1994) demonstrated that a vast majority of lakes are supersaturated with CO2, contributing significantly to regional C cycles as net sources of C to the atmosphere. Long-term data have revealed that dissolved organic carbon (DOC) concentration has been increasing in numerous lakes, initiating a wave of research on the impacts of elevated DOC on lake ecosystem functioning (Monteith et al. 2007). Additionally, through advances in sensor technology, observations of lake metabolic processes have been shown to be meaningfully heterogeneous both within and across lakes (Coloso et al. 2008; Van de Bogert et al. 2012; Solomon et al. 2013; Obrador et al. 2014; Giling et al. 2017). Models are useful for exploring the implications suggested by observations despite that they simplify complexities of reality and focus on key processes regulating system dynamics. Akin to the observational studies mentioned above, several models have also advanced our understanding of lake C cycling. For example, a scaling study demonstrated that lakes are an important component of the global C cycle (Cole et al. 2007), justifying the inclusion of lakes in the Intergovernmental Panel on Climate Change’s Fifth Assessment Report on global C budget (IPCC 2013). A dynamical modeling study demonstrated that allochthonous sources of C can support a large portion of secondary production in lakes through utilization of low-molecular–weight compounds (Berggren et al. 2010). Additionally, a study using first principles of physical limnology showed that gas exchange between lakes and the *Correspondence: [email protected] Additional Supporting Information may be found in the online version of this article. Author contribution Statement: CTS and YTP designed the study; JAZ collected and analyzed the data with input on methodologies from CTS and SEJ; JAZ developed the model with significant input from OH, CTS, and SEJ; JAZ wrote the first draft of the manuscript; and all authors contributed to the final version.