Hydroclimatic Drivers of Dissolved Organic Carbon in Asia's Major Rivers
Bibliographic record
Abstract
Abstract Dissolved organic carbon (DOC) plays a vital role in the global carbon cycle, with river discharge as a major transport mechanism from land to ocean. As the second‐largest freshwater contributor to oceans, Asia experiences significant hydroclimatic variations, yet, observations are patchy across watersheds and climate zones. Here, we compiled 1,593 DOC observations from 40 studies spanning most Asian climate zones to map large‐scale patterns, and tested hydroclimatic controls for a set of representative watersheds (Yangtze, Yellow, Mekong, Ganges‐Brahmaputra, and Rajang) where sufficient time series records exist. Our findings show DOC concentrations peaking near the equator (tropical rainforest) and again above ∼40°N (humid continental dry winter), with tributaries exhibiting higher and more variable levels than mainstems. Hydroclimatic responses were basin‐dependent: DOC‐precipitation correlation was not significant, yet DOC significantly differed across precipitation groups with highest means at low precipitation, indicating nonlinearity and likely thresholds; temperature effects diverged by basin; and soil moisture was a consistent positive driver, especially in Mekong and Yangtze. Overall, this study highlights that DOC behavior in Asia cannot be captured by uniform assumptions across basins and climate zones. As most existing observations are limited to short‐term data sets, the impacts of hydroclimatic change on carbon transport remain uncertain and require long‐term data sets. Future research should take an interdisciplinary approach by integrating hydrology, geomorphology, and climate indicators by fusing remote sensing based observations and advanced analytics, to address differences in DOC behavior and climate challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".