Water use in a riparian cottonwood ecosystem: eddy covariance measurements and scaling along a river corridor
Bibliographic record
Abstract
Abstract The survival of riparian forests depends on water input from their adjacent rivers. There are multiple, additional demands on river water that threatens to reduce the water supply, prompting the question, how much water must be left flowing within a river to sustain the native riparian ecosystems? To address this, we made eddy covariance measurements of riparian cottonwood forest evapotranspiration (ET) in a representative site and used remote sensing and the Penman-Monteith equation to up-scale ET along the 171 km river corridor from the Oldman River Dam to Lethbridge, Alberta. Our study was conducted in two growing seasons with contrasting weather conditions, including one with over-bank flooding (2014). Measured cumulative forest ET from May-September (2014: 451 ± 90 mm; 2015: 411 ± 18 mm) was very similar in the two years despite contrasting May-September cumulative precipitation input (2014: 362 mm; 2015: 181 mm). Integrated over the 56 km 2 area of riparian forest along the Oldman River corridor, the cumulative forest ET during May-September was 19.1 million m 3 (339 mm/season), about 0.9% of the average cumulative Oldman River discharge during 2008–2013. Cottonwood forest ET during May-June was less than 1% of average river flow rates during 2008–2013, but the ratio of ET to average river flow rate increased markedly to peak values of 4–5% in late July and early August. Our analysis indicated that the water-use rates of riparian cottonwood forests were high, even for a broad-leaf deciduous forest functional type, particularly given the modest leaf area index (1.4 m 2 m −2 ) we measured for riparian forests along the Oldman River corridor. The high ET rates were possible because of tree access to alluvial groundwater to support transpiration. Our ET measurements and calculations provided perspective on riparian forest water use in relation to precipitation inputs and total river discharge, knowledge that is important for successfully managing the multiple demands on river water use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".