Using Stable Water Isotopes to Analyze Spatiotemporal Variability and Hydrometeorological Forcing in Mountain Valley Wetlands
Bibliographic record
Abstract
Wetlands in Montane and Subalpine Subregions are increasingly recognized as important hydrologic features that support ecosystem function. However, it is currently not clear how climate trends will impact wetland hydrological processes (e.g., evaporative fluxes) across spatiotemporal scales. Therefore, identifying the factors that influence wetland hydrologic response to climate change is an important step in understanding the sensitivity of these ecosystems to environmental change. We used stable water isotopes of hydrogen and oxygen (δ2H and δ18O), coupled with climate data, to determine the spatiotemporal variability in isotopic signatures of wetland source waters and understand the influence of evaporative fluxes on wetlands in the Kananaskis Valley. Our results show that the primary runoff generation mechanism changes throughout the growing season resulting in considerable mixing in wetland surface waters. We found that evaporative fluxes increased with decreasing elevation and that isotopic values became further removed from meteoric water lines during the late peak- and into the post-growing seasons. These findings suggest that a change in the water balance in favor of enhanced evaporation (due to a warmer and longer summer season than present) will not only lead to greater water loss from the wetlands themselves but may also reduce the water inputs from their catchments.
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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 | 0.001 | 0.001 |
| 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".