An accurate accounting of the terrestrial carbon cycle, along with an advanced understanding of its underlying mechanisms, is required to predict future climate change and ecosystem response (Chapin et al., 2006). Carbon budget estimation and scaling efforts have typically focused on comparing "bottom-up" estimates of carbon stock change from field inventories and biospheric process models with "top-down" estimates of land-atmosphere carbon exchange from inversion modeling frameworks (Battin et al., 2009;. Increasingly recognized, however, is the important role that the lateral transfer of carbon through the aquatic system plays in the overall dynamics of the terrestrial ecosystem (Chapin et al., 2006), and that these "sideways" fluxes must be better characterized in order to balance the carbon budget and reconcile differences between top-down and bottom-up estimates (Webb et al., 2019). Current carbon budget estimation and scaling approaches may only implicitly include these lateral fluxes or ignore them altogether, thus making it difficult or impossible to compare among estimates (Hayes et al., 2018). This lateral carbon flux from soils to inland waters occurs in a few main forms, including dissolved organic carbon (DOC), dissolved inorganic carbon and particulate organic carbon (Battin et al., 2009). DOC is primarily formed by the incomplete decomposition of soil organic carbon, or is exuded by plants (Zarnetske et al., 2018), which can be moved from the soil to inland waters via surface runoff as well as subsurface lateral flow and serves as a key component of the lateral, terrestrial-aquatic carbon flux (Tank et al., 2018). In aquatic ecosystems, DOC can be subject to sedimentation in water bodies or be biotically or abiotically oxidized into CO 2 and released to the atmosphere, with the remaining DOC exported to the ocean (Bai et al., 2020;Battin et al., 2009). Therefore, including the terrestrial-aquatic DOC flux is necessary to improve estimates of broad-scale ecosystem carbon dynamics; however, this flux is often excluded or not well represented in many carbon budget estimation methods (Wei, 2020). For example, eddy-covariance