Evaluating the Effects of Tracer Choice and End‐Member Definitions on Hydrograph Separation Results Across Nested, Seasonally Cold Watersheds
Bibliographic record
Abstract
Abstract Isotope‐based hydrograph separation (IHS) is a widely used method in studies of runoff generation and streamflow partitioning. Challenges in choosing and characterizing appropriate tracers and end‐members have, however led to presumably highly uncertain IHS results. Here we tested the effects of end‐member definitions and tracer choices on IHS results in nested Prairie watersheds of varying size and landscape characteristics. Specifically, the consideration of eight potential “new” water end‐members, eight potential “old” water end‐members, and two stable water isotope tracers led to 80 potential IHS results for each stream water sample. IHS‐related uncertainty was evaluated using a Gaussian error propagation method. Results show that choosing an appropriate “new” water end‐member is most challenging during the freshet: highly variable “old” water fractions associated with high uncertainties were attributed to changing conditions from melting snow only to rain‐on‐snow. In summer and fall, it was rather the choice of an appropriate “old” water end‐member that was most problematic. IHS results obtained using δ 18 O versus δ 2 H as a tracer were significantly different except in the flattest and most wind‐sheltered watersheds examined. Overall, δ 2 H‐based IHS results were more uncertain than their δ 18 O‐based counterparts. Recommendations are therefore made toward careful selection of a tracer and a sampling strategy aimed at characterizing the most appropriate end‐members for IHS, especially when dealing with seasonally cold watersheds.
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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 | 0.003 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".