Controls on evapotranspiration at a subarctic sedge fen
Bibliographic record
Abstract
Abstract In this study, 10 years (1990–99) of summertime data collected at a representative sedge fen in the Hudson Bay Lowland (HBL) are used to investigate the energy and water balance dynamics of subarctic wetlands. The summertime climatic characteristics at the study site during the 10 year study period are also examined. It is shown that mean cumulative summertime precipitation Pavg for the study decade closely approximates the 30 year mean Pavg. However, the mean summertime air temperature Tavg for the study decade is 1 °C higher than the 30 year mean Tavg. To examine the energy and water balance dynamics at the study site, the variation in each of their respective components throughout the study decade is considered. Little variation is observed in cumulative summertime net radiation $Q^{*}_{\rm cum}$ and cumulative summertime ground heat flux QGcum; however, substantial year‐to‐year variation is evident in cumulative summertime water deficit WDcum, cumulative summertime precipitation Pcum, cumulative summertime sensible heat flux QHcum, and cumulative summertime latent heat flux QEcum. It is noted that the variability in QEcum is particularly significant because it consumes the largest proportion of the available summertime energy, and is the largest component of the summertime water balance at this subarctic wetland. Past research has suggested that Q*, P, and T have the most influence on summertime QE at high‐latitude wetlands. To test this hypothesis at our study site, QEcum, Pcum, Tavg and $Q^{*}_{\rm cum}$ from each year in the study decade were examined. It is observed that high QEcum is associated with high Pcum, Tavg and $Q^{*}_{\rm cum}$ , and that low QEcum is associated with low Pcum and Tavg. To identify the hydroclimatological variables that are most responsible for controlling QEcum dynamics at the sedge fen, a stepwise linear regression was performed. This analysis indicates that Pcum and $Q^{*}_{\rm cum}$ are the most important hydroclimatological controls over QEcum. However, it is demonstrated that the variability in Pcum is more responsible than the variability in $Q^{*}_{\rm cum}$ for the variability in QEcum during the study decade because of its higher coefficient of variation. The results of this study have broader applicability to wetlands in other parts of the subarctic ecoregion, including the Mackenzie River Basin (MRB). For example, past studies have shown similarities in the energy balance regimes at wetland sites from the HBL and MRB. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".