Complex Drivers of Riparian Soil Oxygen Variability Revealed Using Self-Organizing Maps

Oxygen (O-2) regulates soil reduction-oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O-2 variability have prompted the use of soil moisture as a proxy for O-2, as O-2 diffusion into soil water is much slower than in soil air. The use of soil moisture alone as a proxy measurement for O-2 could result in inaccurate O-2 estimations. For example, O-2 may 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 O-2) with a machine learning technique, the Self-Organizing Map, to pinpoint suites of soil conditions associated with contrasting O-2 regimes. At two riparian sites in northern Vermont, we found that O-2 levels varied seasonally, and with soil moisture. For example, 47% of low O-2 levels were associated with wet and cool soil conditions, whereas 32% were associated with dry and warm conditions. Contrastingly, the majority (62%) of high O-2 conditions occurred under dry and warm conditions. High soil moisture levels did not always lead to low O-2, as 38% of high O-2 values occurred under wet and cool conditions. Our results highlight challenges with predicting soil O-2 solely based on water content, as variable combinations of soil and hydrologic conditions can complicate the relationship between water content and O-2. This indicates that process-based ecosystem and denitrification models that rely solely on soil moisture to estimate O-2 may need to incorporate other site and climate-specific drivers to accurately predict soil O-2.