Evapotranspiration and energy partitioning across a forest-shrub vegetation gradient in a subarctic, alpine catchment
Bibliographic record
Abstract
As a result of altitude and latitude amplified climate change, widespread changes in vegetation composition, density and distribution have been observed across northern regions. Despite wide documentation of shrub proliferation and treeline advance, few field-based studies have evaluated the hydrological implications of these changes. Quantification of total evapotranspiration (ET) across a range of vegetation gradients is essential for predicting water yield, yet challenging in cold alpine catchments due to heterogeneous land cover, including both boreal forest and shrub taiga ecosystems. Here, we present six years of surface energy balance components and ET dynamics at three sites along an elevational gradient in a subarctic, alpine catchment near Whitehorse, Yukon Territory, Canada. These sites span a gradient of thermal and vegetation regimes, providing a space-for-time comparison for future ecosystem shifts: 1) a low-elevation boreal white spruce forest (~12–20 m), 2) a mid-elevation subalpine taiga comprised of tall, dense willow (Salix) and birch (Betula) shrubs (~1–3 m) and 3) a high-elevation subalpine taiga with short, sparse shrub cover (<0.75 m) and moss, lichen, and bare rock. Eddy covariance instrumentation ran year-round at the forest and during the growing season at the two shrub sites. Total ET decreased and interannual variability increased with elevation, with mean May to September ET totals of 349 (±3) mm at the forest, 249 (±10) mm at the tall, dense shrub site, and 240 (±26) mm at the short, sparse shrub site. Comparatively, over the same period, ET:R ratios were the highest and most variable at the forest (2.19 ± 0.37) and similar at the tall, dense shrub (1.22 ± 0.09) and short, sparse shrub (1.14 ± 0.05) sites. Our results suggest that advances in treeline will increase overall ET and lower interannual variability; however, the large growing season water deficit at the forest indicates strong reliance on soil moisture from late fall and snowmelt recharge. In contrast, ET was considerably less at the cooler higher elevation shrub sites, which exhibited similar ET losses over 6 years despite differences in shrub height and abundance. ET rates between the two shrub sites were similar throughout the year, except during the peak growing season. Greater interannual variability in ET at the short, sparse shrub site indicates the reduced influence of vegetation controls on ET. Results suggest that predicted changes in vegetation type and structure in northern regions will have a considerable impact on water partitioning and will vary in a complex way in response to changing precipitation timing, phase and magnitude, growing season length, and vegetation snow and rain interactions.
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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) | 0.000 | 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 teacher head, 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".