Temporal Coupling of Subsurface and Surface Soil CO 2 Fluxes: Insights From a Nonsteady State Model and Cross‐Wavelet Coherence Analysis

Inferences about subsurface CO 2 fluxes often rely on surface soil respiration ( R soil ) estimates because directly measuring subsurface microbial and root respiration (collectively, CO 2 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 CO 2 fluxes. Coherence between CO 2 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.