Daily heterotrophic respiration model considering the diurnal temperature variability in the soil
Bibliographic record
Abstract
In daily, monthly, and annual respiration models for regional and global applications, the diurnal variation of temperature is generally ignored. As the effect of temperature on respiration is nonlinear, this ignorance may cause considerable errors in respiration estimation, but these errors have not yet been systematically investigated. This is in fact a central issue in temporal scaling of ecosystem models which are often applied in time steps equal to or larger than a day. In this study, we develop an integrated daily heterotrophic respiration model, and demonstrate first theoretically the importance of considering the diurnal amplitude of soil temperature and the vertical soil carbon distribution pattern in daily respiration estimation using the daily mean temperature. Measurements of soil respiration with roots exclusion made in a mature black spruce site in Saskatchewan, Canada, in July–September 2004 are used to validate the model. Daily heterotrophic respiration rates were underestimated by up to 15%, with a mean value of 4.5%, when only the mean daily temperature was used. This underestimation occurred under the conditions that the diurnal temperature amplitude in the forest was less than 12°C and the vertical distribution of organic carbon in the top 15–30 cm was uniform. Based on the integrated daily model, this underestimation at the same site would be 38% if the amplitude increases to 20°C, and in soils with steep vertical carbon distributions with a 20°C diurnal amplitude, it can increase to 44%. The magnitude of this underestimation is theoretically proportional to [ln( Q 10 )] 2 . During the experimental period, the value of Q 10 for heterotrophic respiration was found to be 4.0–4.5. If Q 10 = 2.0, this underestimation is reduced to about 10% at a diurnal temperature amplitude of 20°C.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".