Inter–annual Variability of Hydrological Parameters Improves Simulation of Annual Gross Primary Production
Bibliographic record
Abstract
Parametric uncertainties are among the largest epistemic uncertainties in land surface models (LSM) simulating carbon fluxes, such as gross primary production (GPP). A persistent challenge is that inter–annual variability (IAV) is currently not well-represented in models, which can be attributed to temporally fixed parameters. We parameterized an optimality-based and a light use efficiency (LUE) model (1) per site–year, (2) per site, (3) per site with an additional component in the cost function representing IAV, (4) per plant functional type (PFT), and (5) globally by pooling data from all sites, using hourly eddy-covariance data from 198 sites. Furthermore, parameters representing a given environmental factor in models were grouped to investigate site-year variability of groups of parameters that contributed to improved simulation of IAV of GPP. Temporally varying parameters associated with soil-water availability and drought stress produced a better annual model performance for the optimality-based (median normalized Nash–Sutcliffe efficiency, viz. NNSE: 0.13) and LUE-based (median NNSE: 0.4) models, respectively. We found both high between-PFT and within-PFT variation of parameters, especially for parameters related to hydrology, for example, median available water capacities, which was generally high in broadleaf forests compared to savannas for both models. Though we found that temporally variable parameters can improve annual model performance, the temporal variability of parameters was not as high as their spatial variability. Our study suggests that discovering relations between the spatiotemporal variability of model parameters and their controlling factors can improve the representation of IAV in LSMs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".