Modeling forest carbon cycle using long‐term carbon stock field measurement in the Delaware River Basin
Bibliographic record
Abstract
Abstract Process‐based models are a powerful approach to test our understanding of biogeochemical processes, to extrapolate ground survey data from limited plots to the landscape scale, and to simulate the effects of climate change, nitrogen deposition, elevated atmosphericCO2, increasing natural disturbances, and land‐use change on ecological processes. However, in most studies, the models are calibrated using ground measurements from only a few sites, though they may be extrapolated to much larger areas. Estimation accuracy can be improved if the models are parameterized using long‐term carbon (C) stock data from multiple sites representative of the simulated region. In this study, forest biomass C stocks measured in 61 forested plots located in three research sites in the Delaware River Basin (DRB) were used to modify the PnET‐CNmodel in three ways: (1) Field‐measured mortality rates in each forest type were used to parameterize the wood turnover rate; (2) a numerical approach was used to calibrate the relationship between foliage N and maximum photosynthesis rate; and (3) stand age was incorporated into the model as an input variable, which determines the year of the last disturbance. The results showed that these model modifications improved model performance in capturing the spatial variation of forest C dynamics in theDRBforests. The spatial distribution of forest C pools and fluxes in the three sites was mapped using the modified model. The modified model was also used in experimental scenarios, which predicted that 39% of forest C sequestered over the past decade could be attributed to the combined effects of elevatedCO2and N deposition. This study demonstrated an effective method for using long‐term biometric measurements of forest biomass C stocks to constrain and improve a process‐based ecosystem model at a regional scale. Further research should target improving model parameters that are sensitive to the spatial variation of forest C dynamics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".