The value of water isotope data on improving process understanding in a glacierized catchment on the Tibetan Plateau
Bibliographic record
Abstract
Abstract. This study integrated a water isotope module into the hydrological model THREW which has been successfully used in high and cold regions. Signatures of oxygen stable isotope (18O) of different water inputs and stores were simulated coupling with the simulations of runoff generations. Isotope measurements of precipitation water samples and global precipitation isotope product, as well as assumed constant isotope signature of ice meltwater were used to force the isotope module. Isotope signatures of water stores such as snowpack and subsurface water were updated by an assumed completely mixing procedure. Fractionation effects of snowmelt and evapotranspiration were modeled in a Rayleigh fractionation approach. The isotope-aided model was subsequently applied for the quantifications of runoff components and estimations of mean water travel time (MTT) and mean residence time (MRT) in the glacierized watershed of Karuxung River on the Tibetan Plateau. Model parameters were constrained by three different combinations of observations including a single-objective calibration using streamflow measurement solely, a dual- objective calibration using both streamflow measurement and MODIS estimated snow cover area, and a triple- objective calibration using additionally isotopic composition of stream water. Modeled MTT and MRT was validated by estimate of a tracer-based sine-wave method. Results indicate that: (1) the proposed model performed quite well on simultaneously reproducing the observations of streamflow, snow cover area, and isotopic composition of stream water, despite that only precipitation water samples were available for tracer input; (2) isotope data helped to estimate more plausible contributions of runoff components (CRCs) to streamflow in the melting season, and improved the robustness of MTT and MRT estimations; (3) involving isotope data for the model calibration obviously reduced uncertainties of the quantification of CRCs and estimations of MTT and MRT, through better constraining the strong competitions among different runoff processes induced by meltwater and rainfall. Our results inform high value of water isotope data on improving process understanding in a glacierized basin on the Tibetan Plateau.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".