TY - JOUR
T1 - Temporal Coupling of Subsurface and Surface Soil CO2 Fluxes
T2 - Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis
AU - Samuels-Crow, Kimberly E.
AU - Ryan, Edmund
AU - Pendall, Elise
AU - Ogle, Kiona
N1 - Funding Information:
We thank Jack Morgan for supporting soil research at PHACE, and Dan LeCain and David Smith for maintaining the experimental infrastructure. This manu script is based upon work supported by the U.S. Department of Agriculture Agricultural Research Service Climate Change, Soils and Emissions Program, USDA-CSREES Soil Processes Program (2008-35107-18655), U.S. Department of Energy Office of Science (BER), through the Terrestrial Ecosystem Science pro gram (DE-SC0006973) and the Western Regional Center of the National Institute for Climatic Change Research, and by the National Science Foundation (DEB#1021559). We thank two anonymous reviewers for their assistance in improving the manuscript. The DETECT model, input files, and user manual are available at http://doi.org/10.5281/ zenodo.927501. Driving data, parameter values, and model output for the analysis presented here are available in the supporting information and at http:// jan.ucc.nau.edu/ogle-lab/zzz_ modelDETECT.html in the “JGR exam ple” directory. The Matlab toolbox used for cross wavelet coherence analysis is available at https://www.mathworks. com/matlabcentral/fileexchange/ 47985-cross-wavelet-and-wavelet- coherence.
Publisher Copyright:
©2018. American Geophysical Union. All Rights Reserved.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
KW - carbon cycle
KW - cross-wavelet coherence analysis
KW - free-air CO enrichment
KW - nonsteady state carbon flux
KW - soil respiration
KW - temporal coherence
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U2 - 10.1002/2017JG004207
DO - 10.1002/2017JG004207
M3 - Article
AN - SCOPUS:85046015837
SN - 2169-8953
VL - 123
SP - 1406
EP - 1424
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 4
ER -