Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland
Bibliographic record
Abstract
Abstract Improved predictive capacity of hydrology and surface energy exchange is critical for conserving boreal peatland carbon sequestration under drier and warmer climates. We represented basic processes for water and O2 transport and their effects on ecosystem water, energy, carbon, and nutrient cycling in a process‐based model ecosys to simulate effects of seasonal and interannual variations in hydrology on peat water content, water table depth (WTD), and surface energy exchange of a Western Canadian fen peatland. Substituting a van Genuchten model (VGM) for a modified Campbell model (MCM) in ecosys enabled a significantly better simulation of peat moisture retention as indicated by higher modeled versus measured R2 and Willmot's index (d) with VGM (R2~0.7, d~0.8) than with MCM (R2~0.25, d~0.35) for daily peat water contents from a wetter year 2004 to a drier year 2009. With the improved peat moisture simulation, ecosys modeled hourly WTD and energy fluxes reasonably well (modeled versus measured R2: WTD ~0.6, net radiation ~0.99, sensible heat >0.8, and latent heat >0.85). Gradually declining ratios of precipitation to evapotranspiration and of lateral recharge to discharge enabled simulation of a gradual drawdown of growing season WTD and a consequent peat drying from 2004 to 2009. When WTD fell below a threshold of ~0.35 m below the hollow surface, intense drying of mosses in ecosys caused a simulated reduction in evapotranspiration and an increase in Bowen ratio during late growing season that were consistent with measurements. Hence, using appropriate water desorption curve coupled with vertical‐lateral hydraulic schemes is vital to accurately simulate peatland hydrology and energy balance.
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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 | 0.001 | 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".