Search for: All records

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.