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

Abstract

Inferences about subsurface CO2 fluxes often rely on surface soil respiration (Rsoil) estimates because directly measuring subsurface microbial and root respiration (collectively, CO2 production, STotal) is difficult. To evaluate how well Rsoil serves as a proxy for STotal, 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 STotal and Rsoil 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 Rsoil and STotal is likely valid in dry, sandy soil, but may lead to underestimates of short‐term STotal in semiarid grasslands with fine‐grained and/or wet soil.

Temporal patterns of fine‐root dynamics have little influence on seasonal soil CO2 efflux in a mixed, mesic forest

“…As detailed in Baldwin et al (2017), most of the soils at the SSHCZO fall into the loam to silty loam soil texture categories. Loam and silt loam soils are generally coarse soils and therefore tend to have very little lag (on the scale of hours) between point CO 2 release within the soil profile and soil CO 2 efflux from the surface (Ryan et al, 2018; Samuels‐Crow et al, 2018). Other studies have shown that fine‐root production can have a large effect on soil CO 2 efflux (George et al, 2003; Ryan et al, 1996).…”

“…Alternatively, laboratory‐scale studies where soil CO 2 efflux is measured in conjunction with fine‐root dynamic measurements over both the course of the day and the course of a year could yield more precise insights. Given that finer soil texture soils often have longer lag times with increased soil water content (Ryan et al, 2018; Samuels‐Crow et al, 2018), it would also be useful to quantify when the respiration associated with fine‐root production is measurable at the soil surface via soil CO 2 efflux. This type of study would likely be best suited for laboratory‐scale studies.…”

A Semi‐Mechanistic Model for Partitioning Evapotranspiration Reveals Transpiration Dominates the Water Flux in Drylands

“…We then averaged the temporal coherences across all periods of time to summarize the correlations between the two variables as described in Samuels‐Crow et al. (2018). To more intuitively represent in‐phase (positively correlated) and anti‐phase (negatively correlated) relationships, we modified the resulting R 2 values by multiplying R 2 values of anti‐phase relationships by −1 to create a “temporal coherence index”.…”

“…For example, we tested the weekly to yearly temporal coherence (R 2 ) between each response variable and multiple environmental variables, including lagged correlations, across all weeks in the timeseries. We then averaged the temporal coherences across all periods of time to summarize the correlations between the two variables as described in Samuels-Crow et al (2018). To more intuitively represent in-phase (positively correlated) and anti-phase (negatively correlated) relationships, we modified the resulting R 2 values by multiplying R 2 values of anti-phase relationships by 1 to create a "temporal coherence index".…”