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 P avg for the study decade closely approximates the 30 year mean P avg . However, the mean summertime air temperature T avg for the study decade is 1 °C higher than the 30 year mean T avg . 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 Q Gcum ; however, substantial year‐to‐year variation is evident in cumulative summertime water deficit WD cum , cumulative summertime precipitation P cum , cumulative summertime sensible heat flux Q Hcum , and cumulative summertime latent heat flux Q Ecum . It is noted that the variability in Q Ecum 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 Q E at high‐latitude wetlands. To test this hypothesis at our study site, Q Ecum , P cum , T avg and $Q^{*}_{\rm cum}$ from each year in the study decade were examined. It is observed that high Q Ecum is associated with high P cum , T avg and $Q^{*}_{\rm cum}$ , and that low Q Ecum is associated with low P cum and T avg . To identify the hydroclimatological variables that are most responsible for controlling Q Ecum dynamics at the sedge fen, a stepwise linear regression was performed. This analysis indicates that P cum and $Q^{*}_{\rm cum}$ are the most important hydroclimatological controls over Q Ecum . However, it is demonstrated that the variability in P cum is more responsible than the variability in $Q^{*}_{\rm cum}$ for the variability in Q Ecum 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".