Inland waters (including rivers, streams, lakes, and estuaries) are critical components of the global carbon cycle, acting as active sources and sinks of greenhouse gases (GHGs) such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) (Raymond et al., 2013). Current understanding of aquatic GHG emissions has largely relied on long‑term averages or broad‑scale spatial patterns (Wang et al., 2022), leaving a significant knowledge gap regarding their high‑frequency dynamics and the underlying driving mechanisms. This gap contributes to substantial uncertainties in global GHG budgets and limits the effectiveness of climate‑mitigation strategies that involve aquatic ecosystems.

 

The river systems around Aarhus, Denmark, provide an ideal natural laboratory to address this issue. Characterized by distinct salinity gradients from freshwater headwaters to tidally influenced harbors, and encompassing a mosaic of land‑use types from natural catchments to urbanized areas, these river‑estuary continuums represent a dynamic interface where natural biogeochemical processes and anthropogenic influences interact. Understanding the spatiotemporal patterns of GHG fluxes along such gradients is essential for refining regional carbon accounting and developing targeted management approaches.

 

In this project, we aim to quantify the diurnal and spatial patterns of GHG fluxes in the Aarhus river systems and to identify the key environmental and anthropogenic drivers controlling these patterns. A central goal is to test three key hypotheses: (1) that GHG fluxes exhibit strong, unresolved diurnal patterns; (2) that increasing salinity leads to decreasing GHG concentrations and fluxes, although this signal may be confounded by co‑varying factors such as nutrient loading, discharge, and stream order; and (3) that the observed spatial and temporal patterns emerge from interactions between physicochemical drivers and hydrological dynamics—a relationship that remains methodologically challenging to disentangle from other co‑varying parameters.

 

By integrating continuous sensor data, discrete water chemistry analyses, and advanced statistical modeling, this project will deliver a process‑based understanding of GHG dynamics in temperate aquatic systems. The outcomes will help reduce uncertainties in regional GHG inventories, inform the management of river‑estuary systems under changing environmental conditions, and contribute to more accurate global carbon budgeting.