Impacts of Drought on Water Fluxes and Water‐Use Efficiency in an Age‐Sequence of Temperate Conifer Forests
Bibliographic record
Abstract
ABSTRACT Evapotranspiration (ET) from temperate forests plays a significant role in the regional and global water cycles. However, extreme weather events such as heat and drought are affecting the water use and water use efficiency (WUE) of these forests. Climate change impacts may be more severe in plantation forests where the age of the forest plays a significant role, causing differences in their responses to environmental stresses. This study presents 14 years (2008–2021) of water flux data measured using the eddy covariance technique in an age sequence (83, 48 and 20 years as of 2021) of eastern white pine (Pinus strobus L.) forests in the Great Lakes region in southern Ontario, Canada. The mean annual ET was 465 ± 41, 466 ± 32 and 403 ± 21 mm year−1 in the 83‐, 48‐ and 20‐year‐old stands, respectively, with the highest annual water flux observed in the 83‐year‐ old stand, which was similar to that of the 48‐year‐old stand. Mean annual gross ecosystem productivity (GEP) was 1585 ± 100, 1660 ± 115 and 1634 ± 331 g C m−2 year−1in the 83‐, 48‐ and 20‐year‐old stands, respectively, while mean annual WUE was 3.4 ± 0.4, 3.6 ± 0.4 and 4.0 ± 0.8 g C kg H2O year−1 in the respective stands. Lower ET and relatively higher GEP resulted in the highest WUE in the youngest stand, even though the highest GEP was observed in the middle‐aged stand. Air temperature (Tair) was the dominant control on ET, GEP and WUE in all three different‐aged stands, while drought, characterised as the relative extractable water (REW) in the soil, had a significant impact on ET in the late summer. The results of this study further showed that forest age significantly influenced how forests responded to drought stresses. The younger stand was more efficient in carbon sequestration and water use despite exhibiting greater sensitivity to water stress and higher drought coupling. The long‐term eddy covariance measurements analysed in this study have helped to enhance our understanding of water exchange processes in the temperate conifer forest ecosystems in Eastern North America. Specifically, this work contributes to a better understanding of how different‐aged forests respond to extreme weather events, aiding in the development of new strategies for managing water resources and ensuring water security in the region under a changing climate.
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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 | 0.000 | 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) | 0.000 | 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".