Comparing Bayesian and traditional end-member mixing approaches for hydrograph separation in a glacierized basin
Bibliographic record
Abstract
Abstract. Water tracer data have been successfully used for hydrograph separation in glacierized basins. However, uncertainties in the hydrograph separation are large in these basins, caused by the spatio-temporal variability in the tracer signatures of water sources, the uncertainty of water sampling and the mixing model uncertainty. In this study, we used electrical conductivity (EC) measurements and two isotope signatures (δ18O and δ2H) to label the runoff components, including groundwater, snow and glacier meltwater, and rainfall, in a Central Asia glacierized basin. The contributions of runoff components (CRC) to the total runoff, as well as the corresponding uncertainty, were quantified by two mixing approaches: a traditional end-member mixing approach (TEMMA) and a Bayesian end-member mixing approach. The performance of the two mixing approaches were compared in three seasons, distinguished as cold season, snowmelt season and glacier melt season. Results show that: 1) The Bayesian approach generally estimated smaller uncertainty ranges for the CRC compared to the TEMMA. 2) The Bayesian approach tended to be less sensitive to the sampling uncertainties of meltwater than the TEMMA. 3) Ignoring the model uncertainty caused by the isotope fractionation likely leaded to an overestimated rainfall contribution and an underestimated meltwater share in the melt seasons. Our study provides the first comparison of the two end-member mixing approaches for hydrograph separation in glacierized basins, and gives insights for the application of tracer-based mixing approaches for similar basins.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".