Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost
Bibliographic record
Abstract
Abstract. Numerical simulations of land surface processes are important in order to perform landscape-scale assessments of earth systems. This task is problematic in complex terrain due to (i) high-resolution grids required to capture strong lateral variability, and (ii) lack of meteorological forcing data where they are required. In this study we test a topography and climate processor, which is designed for use with large-area land surface simulation, in complex and remote terrain. The scheme is driven entirely by globally available data sets. We simulate air temperature, ground surface temperature and snow depth and test the model with a large network of measurements in the Swiss Alps. We obtain root-mean-squared error (RMSE) values of 0.64 °C for air temperature, 0.67–1.34 °C for non-bedrock ground surface temperature, and 44.5 mm for snow depth, which is likely affected by poor input precipitation field. Due to this we trial a simple winter precipitation correction method based on melt dates of the snowpack. We present a test application of the scheme in the context of simulating mountain permafrost. The scheme produces a permafrost estimate of 2000 km2, which compares well to published estimates. We suggest that this scheme represents a useful step in application of numerical models over large areas in heterogeneous terrain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".