Characterizing Redox Potential Effects on Greenhouse Gas Emissions Induced by Water‐Level Changes

Abstract

Core Ideas Dynamics of redox potential were induced by water‐table changes in a lysimeter. The redox potential measurements well reflected the different GHG emission sources. Redox potential monitoring is a viable tool for better understanding of GHG emissions. Soil greenhouse gas (GHG) emissions contribute to global warming. To support mitigation measures against global warming, it is important to understand the controlling processes of GHG emissions. Previous studies focusing mainly on paddy rice fields or wetlands showed a strong relationship between soil redox potential and GHG emission (e.g., N2O). However, the interpretation of redox potentials for the understanding of the controlling factors of GHG emission is limited due to the low number of continuous redox measurements in most ecosystems. Recent sensor developments open the possibility for the long‐term monitoring of field‐scale soil redox potential changes. We performed laboratory lysimeter experiments to investigate how changes in the redox potential, induced by changes in the water level, affect GHG emissions from agricultural soil. Under our experimental conditions, we found that N2O emissions followed closely the changes in redox potential. The dynamics of redox potential were induced by changing the water‐table depth in a laboratory lysimeter. Before fertilization during saturated conditions, we found a clear negative correlation between redox potentials and N2O emission rates. After switching from saturated to unsaturated conditions, N2O emission quickly decreased, indicating denitrification as the main source of N2O. In contrast, the emissions of CO2 increased with increasing soil redox potentials. After fertilization, N2O emission peaked at high redox potential, suggesting nitrification as the main production pathway, which was confirmed by isotope analysis of N2O. We propose that redox potential measurements are a viable method for better understanding of the controlling factors of GHG emissions, for the differentiation between different source processes, and for the improvement of process‐based GHG models.