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https://ir.library.oregonstate.edu/concern/articles/5t34sq132

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Abstract
  • This paper examines the sources of uncertainty for the Forced Diffusion (FD) chamber soil respiration (R[subscript s]) measurement technique and demonstrates a protocol for uncertainty quantification that could be appropriate with any soil flux technique. Here we sought to quantify and compare the three primary sources of uncertainty in R[subscript s]: (1) instrumentation error; (2) scaling error, which stems from the spatial variability of R[subscript s]; and (3) random error, which arises from stochastic or unpredictable variation in environmental drivers and was quantified from repeated observations under a narrow temperature, moisture, and time range. In laboratory studies, we found that FD instrumentation error remained constant as R[subscript s] increased. In field studies from five North American ecosystems, we found that as R[subscript s] increased from winter to peak growing season, random error increased linearly with average flux by about 40% of average R[subscript s]. Random error not only scales with soil flux but scales in a consistent way (same slope) across ecosystems. Scaling error, measured at one site, similarly increased linearly with average R[subscript s], by about 50% of average R[subscript s]. Our findings are consistent with previous findings for both soil fluxes and eddy covariance fluxes across other northern temperate ecosystems that showed random error scales linearly with flux magnitude with a slope of ~0.2. Although the mechanistic basis for this scaling of random error is unknown, it is suggestive of a broadly applicable rule for predicting flux random error. Also consistent with previous studies, we found the random error of FD follows a Laplace (double-exponential) rather than a normal (Gaussian) distribution.
  • Keywords: uncertainty, kurtosis, soil respiration, measurement error, variability, random error
  • Keywords: uncertainty, kurtosis, soil respiration, measurement error, variability, random error
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