Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite‐Based Products and Streamflow Observations
Bibliographic record
Abstract
The purpose of this study was to evaluate snow and snowmelt simulated by version 4 of the Community Land Model (CLM4). We performed uncoupled CLM4 simulations, forced by Modem-Era Retrospective Analysis for Research and Applications Land-only (MERRA-Land) meteorological fields. GlobSnow snow cover fraction (SCF), snow water equivalent (SWE) and satellite-based passive microwave (PMW) snowmelt-off day of year (MoD) data were used to evaluate SCF, SWE, and snowmelt simulations. Simulated runoff was then fed into a river routing scheme and evaluation was performed at 408 snow-dominated catchments using gauge observations. CLM4 and GlobSnow snow cover extent showed a strong agreement, especially during the peak snow cover months. Overall there was a good correlation between simulated and observed SWE (correlation coefficient, R = 0.6). Simulated and observed SWE were similar over areas with relatively flat terrain and moderate forest density. The simulated MoD agreed (MoD differences (CLM4-PMW) = +/-7 days) with observations over 39.4% of the study domain. Snowmelt-off occurred earlier in the model compared to the observations over 39.5 % of the domain and later over 21.1% of the domain. Large differences of MoD were seen in the areas with complex terrain and dense forest cover. We also found that, although streamflow seasonal phase was accurately modeled (R=0.9), the peaks controlled by snowmelt were underestimated. Routed CLM4 streamflow tended to occur early (by 10 days on average).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".