Modeling the Snowmelt Runoff Process of the Tizinafu River Basin, Northwest China, with GLDAS Data and Bayesian Uncertainty Analysis
Bibliographic record
Abstract
Abstract The climate of the Tizinafu River basin is characterized by low temperature and sparse precipitation, and snow and glacier melt serve as the main water resource in this area. Modeling the snowmelt runoff process has great significance for local ecosystems and residents. The total streamflow of the Tizinafu River basin was divided into surface streamflow and baseflow. The surface streamflow was estimated using the routing model (RM) with Noah runoff data from Global Land Data Assimilation (GLDAS), and the parameter uncertainty of the RM was quantified through Markov chain Monte Carlo simulation. Additionally, the 10 commonly used baseflow separation methods of four categories [digital filter, hydrograph separation program (HYSEP), baseflow index, and Kalinlin methods] were used to generate the baseflow and were then evaluated by their performance in total streamflow simulation. The results demonstrated that the RM driven by GLDAS runoff data could reproduce the runoff process of the Tizinafu River basin. RM-Hl (local minimum HYSEP method) achieved the best performance in the total streamflow simulation, with Nash–Sutcliffe efficiency (NSE) coefficients of 0.82 and 0.93, relative errors of −0.40% and 10.50%, and observation inclusion ratios C of 62.07% and 68.52% for the calibration and verification periods, respectively. The local minimum HYSEP method was most suitable for describing the baseflow of the Tizinafu River basin among the 10 baseflow separation methods. However, digital filter methods exhibited weak performance in baseflow separation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".