Applications of Forest-DNDC to simulate daily carbon fluxes on the natural and inundated terrestrial ecosystems of Canada
Bibliographic record
Abstract
Forest-DNDC is a process-based biogeochemistry model used to quantify carbon (C) and nitrogen (N) fluxes from both upland and wetland forests. Despite of its usefulness due to low input and parameter requirements, and intermediate modelling scale (day time and landscape), no study has attempted to predict C fluxes on Canadian forests and peatlands. In this study, we evaluate and modify Forest-DNDC for simulating ecosystem C fluxes (Gross Primary Production, Ecosystem Respiration, and Net Ecosystem Production) for mature black spruce forests and ombrotrophic bogs. We also test the performance of a “flooded” version of Forest-DNDC in an attempt to estimate the change in ecosystem C exchanges due to the inundation of forests and peatlands for the creation of hydro-electric reservoirs. The evaluations were conducted for a forest site (NOBS in Manitoba) and a bog site (Mer Bleue bog in Ontario) using 12 and 8 years flux records, respectively. The “flooded” Forest-DNDC behaviour was examined by comparing the model outputs with field observations from a recently impounded boreal forests and peatlands (Eastmain-1 reservoir in Québec). The simulations for the natural ecosystems generated reasonable estimations about daily C fluxes. Over the study periods, mean of daily GPP, ER, and NEP were resulted in 19.3, 17.5, and 1.8 kg C ha-1 d-1 at the black spruce site and 17.0, 14.0, and 3.0 kg C ha-1 d-1 at the bog site, and agreement index (d) between the modelled and measured fluxes in GPP, ER, and NEP were 0.96, 0.96, and 0.75, and 0.94, 0.95, and 0.76 for the forest and peatland, respectively. Daily NEP from “flooded” Forest-DNDC ranged between 0 and -28.6 kg C ha-1 d-1 for the flooded forest and 0 and -9.0 kg C ha-1 d-1 for the flooded peatland, but the comparisons with measured fluxes in both the areas had less agreement (e.g. d < 0.70). However, it is not clear how to directly interpret the observations of the reservoir (i.e. atmosphere exchanges), causing uncertainties in comparison between the flux measurements and model outputs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".