4.7 Article

Spatial and seasonal variations of Q10 determined by soil respiration measurements at a Sierra Nevadan forest

Journal

GLOBAL BIOGEOCHEMICAL CYCLES
Volume 15, Issue 3, Pages 687-696

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2000GB001365

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We examined the spatial and seasonal variation of Q(I0) as an indicator of the temperature sensitivity of soil respiration based on field measurements at a young ponderosa pine plantation in the Sierra Nevada Mountains in California. We measured soil CO2 efflux and soil temperature and moisture in two 20 in x 20 m plots from June 1998 to August 1999. The Q(10) values calculated from soil temperature at 10-cm. depth ranged spatially from 1.21 to 2.63 among 18 chamber locations in the plots. Seasonally, the Q(10) values calculated on the basis of the average Soil CO2 efflux and temperature (10 cm) across the sites could vary from 1.05 to 2.3. Q(10) and soil temperature are negatively correlated through a simple linear relationship with R-2 values of 0.45, 0.40, and 0.54 for soil temperature at 5-, 10-, and 20-cm depth, respectively. However, Q(10) and soil moisture are positively correlated with R-2 values of 0.81, 0.86, and 0.51 for soil temperature at 5-, 10-, and 20-cm depth, respectively. Q(10) values derived from temperatures at different soil depths also showed considerable variation along the vertical dimension. Q(10) had a large seasonal variation with the annual minimum occurring in midsummer and the annual maximum occurring in winter. Seasonal values of Q(10) depended closely on both soil temperature and moisture. Soil temperature and moisture explained 93% of the seasonal variation in Q(10). The spatial variation of Q(10) had significant influences on the estimation of soil CO2 efflux of the ecosystem. These variations tended to affect the seasonality of the Soil CO2 efflux more than the annual average. The variations of Q(10) and its dependence on soil moisture and temperature have important implications for regional and global ecosystem carbon modeling, in particular for predicting the responses of terrestrial ecosystems to future global warming.

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