Bibliographic record
Abstract
Abstract. This study aimed to improve runoff simulations and explore deep soil hydrological processes for a watershed in the center of the Loess Plateau (LP), China. This watershed, the Wuding River Basin (WRB), has very complex topography, with soil depths ranging from 0 to 197 m. The hydrological model used for our simulations was Community Land Model version 5 (CLM5) developed by the National Center for Atmospheric Research. Actual soil depths and river channels were incorporated into CLM5 to realistically represent the physical features of the WRB. Through sensitivity tests, CLM5 with 150 soil layers with the observed variable soil depths produced the most reasonable results and was adopted for this study. Our results showed that CLM5 with actual soil depths significantly suppressed unrealistic variations of the simulated subsurface runoff when compared to the default simulations. In addition, when compared with the default version with 20 soil layers, CLM5 with 150 soil layers slightly improved runoff simulations but generated simulations with much smoother vertical water flows that were consistent with the uniform distribution of soil textures in our study watershed. The runoff simulations were further improved by the addition of river channels to CLM5, where the seasonal variability of the simulated runoff was reasonably captured. Moreover, the magnitude of the simulated runoff remarkably decreased with increased soil evaporation by lowering the soil water content threshold, which triggers surface resistance. The lowered threshold was consistent with the loess soil, which has a high sand component. Such soils often generate stronger soil evaporation than soils dominated by clay. Finally, with the above changes in CLM5, the simulated total runoff matched very closely with observations. When compared with those for the default runoff simulations, the correlation coefficient, root mean square error, and Nash–Sutcliffe coefficient for the improved simulations changed dramatically from 0.02, 10.37 mm, and −12.34 to 0.62, 1.8 mm, and 0.61. The results in this study provide strong physical insight for further investigation of hydrological processes in complex terrain with deep soils.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.412 | 0.347 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".