Enhancing Winter Wheat Representation in Noah‐MP‐Crop for Improved Dynamic Crop Growth Simulation in the North China Plain
Bibliographic record
Abstract
Abstract Explicitly representing the world's most frequently cultivated winter wheat in land surface model (LSM) is important for understanding carbon and energy cycling over cropland and its interactions with climate, which is crucial for global food security. However, in the latest version of Noah‐MP‐Crop LSM, winter wheat is significantly underrepresented. This study improved the winter‐wheat parameterization in Noah‐MP‐Crop model by optimizing the phenological scheme, incorporating vernalization process, and calibrating several key parameters associated with winter wheat photosynthesis and carbon allocations. Focusing on the North China Plain as area representative region, model performance in simulating crop dynamic growth, carbon flux, and energy fluxes was validated at both site and regional scales. Results showed that the simulated phenological development matched well with the real‐world phenological records. A comparison between the simulated results by the default and developed parameterizations revealed the significant improvements in the reproductions of leaf area index (LAI) and gross primary production (GPP). The determination coefficient (R2) value of GPP was increased from 0.15 to 0.46 to 0.39–0.91. Simulations of energy fluxes showed smaller improvements, with R2 values increasing from 0.46 to 0.67 to 0.61–0.84 for latent heat (LE) and 0.18–0.55 to 0.25–0.61 for sensible heat. Additionally, the mean average error of net radiation was reduced. Improvements in spatial and temporal variations of LAI, GPP, and LE in regional simulation were also observed. This work can facilitate incorporating winter wheat cultivation and its interactions with climate system, particularly when coupling the Noah‐MP‐Crop model with the widely used Weather Research and Forecasting model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".