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

This is the authors' abstract. We don't add key points for this paper.

Oxygen (O2) is a key regulator of soil reduction-oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O2 variability have prompted the use of soil moisture as a proxy for soil O2, based on the low solubility of O2 in water. Due to seasonal shifts in soil O2 depletion mechanisms, the use of soil moisture alone as a proxy measurement could result in inaccurate O2 estimations. For example, soil O2 may remain high during cool months when soil respiration rates are low. We analyzed high-frequency sensor data (e.g., soil moisture, temperature, CO2, O2) with a machine learning technique, the Self-Organizing Map, to pinpoint suites of soil conditions that are associated with contrasting O2 regimes. At two low-lying riparian sites in contrasting land use and topographic settings of northern Vermont, we found that soil O2 levels varied seasonally, and with soil moisture. For example, forty-seven percent of low O2 levels were associated with cool and wet soil conditions, whereas 32% were associated with warm and dry conditions. Contrastingly, the majority (62%) of high O2 conditions occurred under warm and dry conditions. High soil moisture levels did not always lead to low O2, however, as 38% of high O2 values occurred under cool and wet conditions. Our results highlight challenges associated with predicting soil O2 solely based on soil moisture, as variable combinations of soil and site-specific hydrologic conditions can complicate the relationship between soil water content and O2. This indicates that process-based ecosystem and denitrification models that rely solely on soil moisture to estimate O2 availability will, in some cases, need to incorporate other site and climate-specific drivers to accurately predict soil O2.