Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally
Bibliographic record
Abstract
Abstract Uncertainty in ground heat flux (G) means that evaluation of the other terms in the surface energy balance (e.g., latent and sensible heat fluxes (LE and H)) remains problematic. Algorithms that calculate LE and H require available energy, the difference between net radiation, RNET, and G. There are a wide range of approaches to model G for large‐scale applications, with a subsequent wide range of estimates and accuracies. We provide the largest review of these methods to date (N = 6), evaluating modeled G against measured G from 88 FLUXNET sites. The instantaneous midday variability in G is best captured by models forced with net radiation, while models forced by temperature show the least error at both instantaneous and daily time scales. We produce global decadal data sets of G to illustrate regional and seasonal sensitivities, as well as uncertainty. Global model mean midmorning instantaneous G is highest during September, October, and November at 63.42 (±16.84) Wm−2, while over December, January, and February G is lowest at 53.86 (±18.09) Wm−2 but shows greater intermodel uncertainty. Results from this work have the potential to improve evapotranspiration estimates and guide appropriate G model selection and development for various land uses.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".