Temporal Coupling of Subsurface and Surface Soil CO₂ Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis

Inferences about subsurface CO₂ fluxes often rely on surface soil respiration (R_soil) estimates because directly measuring subsurface microbial and root respiration (collectively, CO₂ production, S_Total) is difficult. To evaluate how well R_soil serves as a proxy for S_Total, we applied the nonsteady state DEconvolution of Temporally varying Ecosystem Carbon componenTs model (0.01-m vertical resolution), using 6-hourly data from a Wyoming grassland, in six simulations that cross three soil types (clay, sandy loam, and sandy) with two depth distributions of subsurface biota. We used cross-wavelet coherence analysis to examine temporal coherence (localized linear correlation) and offsets (lags) between S_Total and R_soil and fluxes and drivers (e.g., soil temperature and moisture). Cross-wavelet coherence revealed higher coherence between fluxes and drivers than linear regressions between concurrent variables. Soil texture and moisture exerted the strongest controls over coherence between CO₂ fluxes. Coherence between CO₂ fluxes in all soil types was strong at short (~1 day) and long periods (>8 days), but soil type controlled lags, and rainfall events decoupled the fluxes at periods of 1–8 days for several days in sandy soil, up to 1 week in sandy loam, and for a month or more in clay soil. Concentrating root and microbial biomass nearer the surface decreased lags in all soil types and increased coherence up to 10% in clay soil. The assumption of high temporal coherence between R_soil and S_Total is likely valid in dry, sandy soil, but may lead to underestimates of short-term S_Total in semiarid grasslands with fine-grained and/or wet soil.