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  1. Abstract

    Oxygen (O2) regulates soil reduction‐oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O2variability have prompted the use of soil moisture as a proxy for O2, as O2diffusion into soil water is much slower than in soil air. The use of soil moisture alone as a proxy measurement for O2could result in inaccurate O2estimations. For example, O2may remain high during cool months when soil respiration rates are low. We analyzed high‐frequency sensor data (e.g., soil moisture, CO2, gas‐phase soil pore O2) with a machine learning technique, the Self‐Organizing Map, to pinpoint suites of soil conditions associated with contrasting O2regimes. At two riparian sites in northern Vermont, we found that O2levels varied seasonally, and with soil moisture. For example, 47% of low O2levels were associated with wet and cool soil conditions, whereas 32% were associated with dry and warm conditions. Contrastingly, the majority (62%) of high O2conditions occurred under dry and warm conditions. High soil moisture levels did not always lead to low O2, as 38% of high O2values occurred under wet and cool conditions. Our results highlight challenges with predicting soil O2solely based on water content, as variable combinations of soil and hydrologic conditions can complicate the relationship between water content and O2. This indicates that process‐based ecosystem and denitrification models that rely solely on soil moisture to estimate O2may need to incorporate other site and climate‐specific drivers to accurately predict soil O2.

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