PNNL

Abstract

Dissolved oxygen (DO) is a key biogeochemical control in coastal systems, and its concentration and drivers vary markedly through time and space. This makes it difficult to accurately represent coastal DO and associated biogeochemical processes in Earth system models, limiting our ability to predict how these systems will respond to global change. We used high-frequency (5-minute) data collected across the terrestrial-aquatic interface (TAI) of a tidal creek in the Pacific Northwest, USA and Random Forest machine learning models to quantify the importance of three categories of environmental drivers (Aquatic, Climatic, and Terrestrial) of DO variability at the TAI. We selected two 5-month datasets representing Summer and Winter seasonal periods to test two hypotheses on the dominant drivers of DO at the coastal TAI. We found that the Terrestrial driver—characterized by long periods of anaerobic conditions and episodic pulses in DO after floods—was most important during the Winter, while the Aquatic driver—characterized by variability over tidal, diel, and lunar cycles—was most important during the Summer. We explored how future air temperature and sea level rise scenarios could alter the drivers of DO variability using a cumulative sums driver-response framework. Our results suggest that Aquatic and Climatic drivers may become increasingly important during the Summer, potentially linked to changes in metabolic regimes and sea level, while the Terrestrial driver may become increasingly important during the Winter. Our approach highlights useful methods for understanding the spatiotemporal complexity of oxygen across the coastal TAI and quantifying the relative importance of distinct environmental drivers.