Isotopic time‐series partitioning of streamflow components in wetland‐dominated catchments, lower Liard River basin, Northwest Territories, Canada
Bibliographic record
Abstract
Abstract The distribution of stable water isotopes provides valuable insight into runoff generation processes in subarctic wetland regions of the Mackenzie River basin, a major freshwater contributor to the Arctic Ocean and the focus of intensive hydrological research as part of Canada's contribution to the Global Energy and Water Cycle Experiment (GEWEX). This article describes a streamflow hydrograph separation analysis carried out over three complete annual cycles (1997–1999) for five subarctic catchments ranging in size from 202 to 2050 km 2 situated near the confluence of the Liard and Mackenzie rivers. This heterogeneous landscape, characterized by extensive wetlands (fen and bog), shallow lakes and widespread discontinuous permafrost, is representative of vast flow‐contributing areas of the upper Mackenzie Valley, and is suspected to be highly sensitive to climate variability and change. We document seasonal patterns and interannual variability in the isotopic composition of local streamflow, attributable to mixing of three distinctly labelled flow sources, namely groundwater, surface water plus rain, and direct snowmelt, and apply these isotopic signals to partition sources and their temporal variability. Although groundwater input is the dominant and most persistent streamflow source in all five catchments throughout the year, direct snowmelt runoff via surface and shallow subsurface pathways (during spring freshet) and surface waters from lakes and wetlands situated in low‐lying areas of the basins (during summer and fall) are also significant seasonal contributors. Catchment‐specific differences are also apparent, particularly in the generation of snowmelt runoff, which is more attenuated in fen‐dominated than in bog‐dominated catchments. The data set additionally reveals notable interannual variability in snow isotope signatures and snow water equivalent, apparently enhanced by the 1998 El Niño event. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".