Noah Modelling of the Permafrost Distribution and Characteristics in the West Kunlun Area, Qinghai‐Tibet Plateau, China
Bibliographic record
Abstract
Abstract The Noah land surface model (LSM) can simulate well the hydrological and thermal processes of permafrost in the Qinghai‐Tibet Plateau (QTP) and provides more permafrost metrics than those of statistical empirical models. The aim of this study was to develop a prototype for permafrost modelling by Noah and validate the model with field data. This was accomplished by modifying Noah, introducing a new thermal roughness scheme, a parameter calibration method and extending the simulation depth to allow for soil heterogeneity. The modified Noah LSM was validated using observations from the Tanggula meteorological station. Key permafrost metrics were simulated, including mean annual ground temperature (MAGT) at the depth of zero annual amplitude (DZAA), active layer thickness (ALT) and ground ice content of the West Kunlun area in the QTP. The permafrost distribution of the West Kunlun was mapped using the simulated MAGT and compared to a permafrost distribution map based on field observations. Data from ten boreholes were used for verification. The simulation error of the MAGT is less than 1.0 °C for eight boreholes, and the ALT simulations have relative errors of less than 25 per cent for seven boreholes. The Kappa coefficient for the two maps is 0.70. Permafrost characteristics including the distribution of different permafrost types, DZAA, ALT, MAGT and ground ice content in the West Kunlun are strongly influenced by altitude and the local environment. Such permafrost modelling can be extended to the rest of the QTP. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".