Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite‐Based Products and Streamflow Observations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 it