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

Abstract

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.

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

“…The high frequency soil sensor data used to assess the relationship between soil moisture and soil oxygen (Lancellotti et al, 2021) are available in the Environmental Data Initiative repository under Creative Commons license via 10.6073/pasta/f62c630ff7a917eae8d320f8400ad2ec without a registration requirement. The R scripts required to perform the Self-Organizing Map (SOM) analysis and visualize the SOM results (Lancellotti et al, 2022) are available on the Zenodo repository under the Creative Commons license via 10.5281/ zenodo.7271714 at https://doi.org/10.5281/zenodo.7271714 without a registration requirement. The software required to perform the analyses presented in this study (R and R studio) are open-source and are available for free at https://posit.co/download/rstudio-desktop/.…”