The incorporation of an organic soil layer in the Noah-MP Land Surface Model and its evaluation over a Boreal Aspen Forest
Bibliographic record
Abstract
Abstract. In this study, the multi-parameterization version of the Noah land-surface model (Noah-MP) was used to investigate the impact of adding a forest-floor organic soil layer on the simulated surface energy and water cycle components at a boreal aspen forest. The test site selected is BERMS Old Aspen Flux (OAS) field station in central Saskatchewan, Canada. The selection of different parameterization schemes for each process within the current Noah-MP model significantly affected the simulation results. The best combination options without incorporating organic soil is referred as the control experiment (CTL). By including an organic-soil parameterization within the Noah-MP model for the first time, the verification results (OGN) against site show significantly improved performance of the model in surface energy fluxes and hydrology simulation due to the lower thermal conductivity and greater porosity of the organic soil. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and year, and those effects are more prominent in summer and in deep soils. For drought years, the OGN simulation substantially modified the partition between direct soil evaporation and vegetation transpiration. For wet years, the OGN simulated latent heat fluxes are similar to CTL except for spring season where OGN produced less evaporation. The impact of the organic soil on sub-surface runoff is substantive with much higher runoff throughout the season.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".