Seasonal patterns in soil surface CO₂ flux under snow cover in 50 and 300 year old subalpine forests
Soil CO₂ flux can contribute as much as 60-80% of total ecosystem respiration in forests. Although considerable research has focused on quantifying this flux during the growing season, comparatively little effort has focused on non-growing season fluxes. We measured soil CO₂ efflux through snow in 5...
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Veröffentlicht in: | Biogeochemistry 2005-03, Vol.73 (1), p.93-107 |
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Zusammenfassung: | Soil CO₂ flux can contribute as much as 60-80% of total ecosystem respiration in forests. Although considerable research has focused on quantifying this flux during the growing season, comparatively little effort has focused on non-growing season fluxes. We measured soil CO₂ efflux through snow in 50 and ∼300 year old subalpine forest stands near Fraser CO. Our objectives were to quantify seasonal patterns in wintertime soil CO₂ flux; determine if differences in soil CO₂ flux between the two forest ages during the growing season persist during winter; and to quantify the sample size necessary to discern treatment differences. Soil CO₂ flux during the 2002-2003 and 2003-2004 snow season averaged 0.31 and 0.35 μmols$\text{m}^{-2}\text{s}^{-1}$for the young and old forests respectively; similar to the relative difference observed during summer. There was a significant seasonal pattern of soil CO₂ flux during the winter with fluxes averaging 0.22 μmols$\text{m}^{-2}\text{s}^{-1}$in December and January and increasing to an average of 0.61 μmols$\text{m}^{-2}\text{s}^{-1}$in May. Within-plot variability for measurements used in calculating flux was low. The coefficients of variation (CV) for CO₂ concentration, snowpack density, and snow depth were 17, 8 and 14%, respectively, yielding a CV for flux measurements within-plot of 29%. A within plot CV of 29% requires 8 sub-samples per plot to estimate the mean flux with a standard error of ± 10% of the mean. Variability in CO₂ flux estimates among plots (size = 400 m) was similar to that within plot and was also low (CV = ∼28%). With a CV of 28% among plots, ten plots per treatment would have a 50% probability of detecting a 25% difference in treatment means for α = 0.05. |
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ISSN: | 0168-2563 1573-515X |
DOI: | 10.1007/s10533-004-1990-0 |