Inter-Monthly Variations in CO2 and CH4 Fluxes in a Temperate Forest: Coupling Dynamics and Environmental Drivers

Climate change, driven largely by anthropogenic greenhouse gas emissions, is a major global issue. Long-term high-frequency measurements of gas fluxes remain limited, especially outside the growing season. This study addresses two key gaps: the absence of continuous annual data capturing diurnal and seasonal variations, and the biases from suboptimal sampling timing. Using automated chambers, we monitored soil CO2 and CH4 fluxes throughout 2016 in a temperate forest on Changbai Mountain, China. Our results showed a strong negative correlation between annual CO2 and CH4 fluxes, with a slope of −0.21 and R2 of 0.70. This relationship persisted from March to November but was absent during the winter and April. Both gases exhibited the largest diurnal variations in summer. Statistical analysis identified 16:00 as the optimal single sampling time for estimating daily mean fluxes in most months. CO2 fluxes were primarily governed by temperature but modulated by VWC (soil volumetric water content). They were suppressed during summer drought and enhanced during winter freeze–thaw cycles. CH4 uptake rates were strongly dependent on VWC throughout the growing season, while their temperature response underwent a reversal from positive in summer to negative in winter. Decision tree analysis revealed nonlinear threshold responses. CO2 fluxes exhibited three temperature thresholds between 5.30 and 15.64 °C and two VWC thresholds between 0.30 and 0.42 m3 m−3. CH4 fluxes showed five temperature thresholds ranging from 2.34 to 15.71 °C and seven VWC thresholds from 0.11 to 0.44 m3 m−3. The strongest anticorrelation between CH4 flux and temperature occurred at intermediate VWC levels. This study provides detailed characteristics of greenhouse gas fluxes based on complete annual high-frequency data. It emphasizes the importance of year-round monitoring and offers improved sampling strategies and mechanistic insights for better flux monitoring and climate prediction.