Isotopic constraints on water balance and evapotranspiration partitioning in gauged watersheds across Canada
Bibliographic record
Abstract
During 2013–2019, we conducted a Canada-wide program of streamflow sampling for the analysis of stable isotopic composition (18O/16O and 2H/1H), providing the first comprehensive survey for gauged watersheds across Canada ranging from 10 to 10,000 km2. A watershed-based assessment of vapour and runoff partitioning is presented for 103 watersheds across a diverse range of climate and land cover types, spanning 25° latitude and 86° longitude. An isotope-based methodology is applied to estimate evaporation/inflow (E/I) and transpiration/evapotranspiration (T/ET) utilizing offset between isotope values in streamflow and precipitation, augmented by regional climate reanalysis data. Isotopic enrichment in streamflow serves to differentiate direct, abiotic evaporation, mainly arising from open water evaporation from lakes and wetlands, from transpiration by natural vegetation and cropland, which has previously been recognized as principally non-fractionating. Sensitivity analysis suggests only a minor influence of interception losses on T/ET. Systematic variations in evaporation losses, transpiration losses and gauged runoff are revealed across the major hydrometric regions of Canada. Calculations suggest that E/I ranged from 2 to 60 %, while T/ET ranged from 25 to greater than 95 % across the watersheds. A new water loss classification is introduced which reveals that 19 of 103 watersheds were runoff-dominated, 54 were transpiration-dominated, 5 were evaporation-dominated, and 27 had more than one dominant water loss mechanism.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".