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

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      https://www.riss.kr/link?id=O119634944

      • 저자
      • 발행기관
      • 학술지명
      • 권호사항
      • 발행연도

        2018년

      • 작성언어

        -

      • Print ISSN

        2169-8953

      • Online ISSN

        2169-8961

      • 등재정보

        SCOPUS;SCIE

      • 자료형태

        학술저널

      • 수록면

        1406-1424   [※수록면이 p5 이하이면, Review, Columns, Editor's Note, Abstract 등일 경우가 있습니다.]

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      다국어 초록 (Multilingual 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...

      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.
      Soil CO2, which is produced underground by roots and microbes, is a major part of the global carbon cycle. There are large uncertainties over how soil CO2 will change as global temperatures and atmospheric CO2 rise. One source of uncertainty is how quickly soil CO2 moves from the sites where it is produced underground to the surface where it is released to the atmosphere. In this paper, we use a numerical model to test the common assumption that CO2 produced underground is released immediately to the atmosphere. We found that this assumption is valid when soil is coarse and dry, but there are delays between subsurface CO2 production and release to the atmosphere when the soil has a fine texture and/or is wet.


      Soil respiration is a good proxy for concurrent subsurface CO2 production in coarse‐textured, dry soil with shallow roots and microbes
      Soil texture exerts the largest control on the temporal coherence between subsurface and surface soil CO2 fluxes
      Lag times between subsurface and surface CO2 fluxes increase when soil is wet and/or subsurface biota are concentrated deeper in the soil

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