Comparing Stream Metabolism in Two Tributaries and Downstream of Their Confluence

Abstract:

Ecosystem metabolism reflects the total carbon fixed (gross primary productivity; GPP) and organic carbon respired as CO₂ (ecosystem respiration; ER). Stream metabolism varies seasonally and responds to disturbances (e.g., landscape alterations, nutrient loading, and flow changes), making it a valuable metric for monitoring ecosystems. However, due to environmental factors differing spatially across a network, selecting a site for optimal monitoring of a heterogeneous landscape presents a challenging task for sensor-derived science. Further, when streams draining catchments with different landscapes meet and mix at confluences, non-additive effects (i.e., priming) may influence downstream metabolism and water quality. To better understand how catchment land use, seasons, and mixing at confluences affect whole-stream metabolism, we deployed dissolved oxygen sensors in two adjacent streams in Blacksburg, VA (Stroubles Creek (SC), Walls Branch (WB)), as well as downstream of their confluence during two seasons in 2018 and 2019. We then modeled metabolism in WB and SC tributaries using a single-station inverse modeling approach over one week for each seasonal deployment. We predicted that seasonal variation in metabolism across streams would be influenced by changes in flow, sunlight, and terrestrial inputs (e.g. leaf litter, road salt) to each stream over the course of the year. Metabolism in both seasons was higher in SC (GPP from 0.5 to 5.6 g O2 m-2 d-1; ER from -1.7 to -10.7 g O2 m-2 d-1) than in WB (GPP from 0.01 to 0.5 g O2 m-2 d-1; ER from -0.1 to -4.8 g O2 m-2 d-1). Additionally, metabolism was highest in SC in the fall and WB in the summer, with rates in SC varying more across seasons than those in WB. We are developing a two-station inverse metabolism model to investigate how tributary inputs influence reach-scale metabolism within a confluence. We expect that downstream metabolism will not reflect a simple combination of the rates of its tributaries due to non-additive effects of mixing streams that drain different landscapes. Our work examining the influence of tributaries on downstream metabolism will allow us to better understand how anthropogenic factors, such as catchment land use, are reflected in the metabolism of entire stream networks.