Seasonality and Discharge as Key Drivers of Headwater Stream Carbon Dioxide Emissions in the Landscape Carbon Budget

Abstract

Quantifying carbon losses from inland waters has emerged as an uncertainty in our understanding of the global carbon cycle. Streams and rivers are of particular interest because of their potential to emit carbon dioxide (CO2) to the atmosphere with some estimates predicting riverine carbon emissions will alter the calculation of terrestrial net ecosystem exchange (NEE), the balance between how much carbon land ecosystems absorb and how much they release. Headwater streams, small tributaries of rivers at the highest end of a watershed, are especially important when quantifying these CO2 emissions and carbon losses because of their tight coupling to the terrestrial environment and high turbulence. Heterogeneity within headwater stream networks, both spatially and temporally, makes measuring and upscaling these emissions challenging because measurements of carbon dioxide in streams are often limited to a few monitoring points. In this dissertation, we sought to fill knowledge gaps regarding spatial and temporal variability in CO2 emissions across a range of biomes. In Chapter 2, we demonstrated how under high flow conditions, a stream network in the Pacific Northwest, can have much greater total carbon emissions than during low flow conditions (1.22 Mg C day−1 vs. 0.034 Mg C day−1). Increased stream network area, higher gas exchange, and greater terrestrial connectivity all contributed to these increased emissions in our stream network model. We found these carbon emissions during high flow in November accounted for a much larger percentage of NEE than during base flow in August (54% vs. 0.62%), emphasizing the need to better quantify carbon emission during flow events. In Chapter 3, we expanded this analysis by modeling carbon emissions from five headwater stream networks in different biomes, incorporating stream network extent to account for dynamic flow and a stream network model to account for spatial and temporal variations in CO2 emissions on an annual scale. We found that while accounting for the extent of the stream network due to drying does not change modeled annual emissions substantially (0.06-4.3%), it does change the timing and spatial distribution of emissions and CO2 concentrations. We found discharge was the main driver of emissions at all sites, with 50% of carbon emissions occurring in the top 3-29% of discharge conditions. Spatially, our analysis highlighted that first-order streams consistently produced higher areal emissions compared to higher-order streams, attributed to steeper slopes and connectivity to the source of pCO2, terrestrial soils and groundwater. Finally, in Chapter 4, we estimated CO2 emissions from a stream in an agricultural catchment, an understudied biome in regards to carbon dynamics. We found that in this low-lying catchment with high nutrient and organic matter inputs, the expected coupling between discharge and CO2 emissions was dampened because of a weaker relationship between slope and gas exchange velocity. Instead, we found the hydrologic regime regulated the magnitude of emissions by regulating the source, namely the higher in-stream metabolism contribution (46%) to emissions at a site with high nutrient and organic matter inputs. Across the three chapters, we demonstrate how the hydrologic regime of a stream network governs the timing, source, and magnitude of CO2 emissions. We also show that carbon dynamics in headwater streams vary across networks, influenced by differences in biome, topography, land use, and geology, highlighting the complexity of accurately quantifying carbon losses from these systems.