Modeling Global Riverine DOC Flux Dynamics From 1951 to 2015
Bibliographic record
Abstract
Abstract Climate change has a profound impact on the global carbon cycle, including effects on riverine carbon pools, which connect terrestrial, oceanic, and atmospheric carbon pools. Until now, terrestrial ecosystem models have rarely incorporated riverine carbon components into global carbon budgets. Here we developed a new process‐based model, TRIPLEX‐HYDRA (TRIPLEX‐hydrological routing algorithm), that considers the production, consumption, and transport processes of nonanthropogenic dissolved organic carbon (DOC) from soil to river ecosystems. After the parameter calibration, model results explained more than 50% of temporal variations in all but three rivers. Validation results suggested that DOC yield simulated by TRIPLEX‐HYDRA has a good fit (R2 = 0.61, n = 71, p < 0.001) with global river observations. And then, we applied this model for global rivers. We found that mean DOC yield of global river approximately 1.08 g C/m2 year, where most high DOC yield appeared in the rivers from high northern or tropic regions. Furthermore, our results suggested that global riverine DOC flux appeared a significant decrease trend (average rate: 0.38 Pg C/year) from 1951 to 2015, although the variation patterns of DOC fluxes in global rivers are diverse. A decreasing trend in riverine DOC flux appeared in the middle and high northern latitude regions (30–90°N), which could be attributable to an increased flow path and DOC degradation during the transport process. Furthermore, increasing trend of DOC fluxes is found in rivers from tropical regions (30°S–30°N), which might be related to an increase in terrestrial organic carbon input. Many other rivers (e.g., Mississippi, Yangtze, and Lena rivers) experienced no significant changes under a changing environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".