Modelling effects of seasonal variation in water table depth on net ecosystem CO <sub>2</sub> exchange of a tropical peatland
Bibliographic record
Abstract
Abstract. Seasonal variation in water table depth (WTD) determines the balance between aggradation and degradation of tropical peatlands. Longer dry seasons together with human interventions (e.g. drainage) can cause WTD drawdowns making tropical peatland C storage highly vulnerable. Better predictive capacity for effects of WTD on net CO2 exchange is thus essential to guide conservation of tropical peat deposits. Mathematical modelling of basic eco-hydrological processes under site-specific conditions can provide such predictive capacity. We hereby deploy a mathematical model ecosys to study effects of seasonal variation in WTD on net ecosystem productivity (NEP) of an Indonesian peatland. We simulated lower NEPs (~ –2 g C m–2 d–1) during rainy seasons with shallow WTD, higher NEPs (~ +1 g C m–2 d–1) during early dry seasons with intermediate WTD and again lower NEPs (~ –4 g C mm–2 d–1) during late dry seasons with deep WTD during 2002–2005. These values were corroborated by regressions (P < 0.0001) of hourly modelled vs. eddy covariance (EC) measured net ecosystem CO2 fluxes which yielded R2 > 0.8, intercepts approaching 0 and slopes approaching 1. We also simulated a gradual increase in annual NEPs from 2002 (−609 g C m–2) to 2005 (−373 g C m–2) with decreasing WTD which was corroborated by EC-gap filled annual NEP estimates. These WTD effects on NEP were modelled from basic eco-hydrological processes including microbial and root oxidation-reduction reactions driven by soil and root O2 transport and uptake which in turn drove soil and plant C, N and P transformations within a soil-plant-atmosphere water transfer scheme driven by water potential gradients. This modelling should therefore provide a predictive capacity for WTD management programs to reduce tropical peat degradation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".