On the Cause of Large Daily River Flow Fluctuations in the Mekong River
Bibliographic record
Abstract
Abstract. Natural fluctuations in river flow are central to the ecosystem productivity of basins, yet significant alterations in daily flows pose threats to the integrity of the hydrological, ecological, and agricultural systems. In the dammed Mekong River, the attribution of these large daily flow changes to upstream regions remains mechanistically unexamined, a factor blamed on challenges in estimating the time required for large daily shifts in upstream river flow to impact the downstream regions. Here, we address this by integrating a newly developed sub-basin modeling framework that incorporates 3D hydrodynamic, response time, and hydrological models. This integration allows us to estimate the time required between two hydrological stations and to distinguish the contribution of sub-basins and upstream regions to large daily river flow alterations. Findings revealed a power correlation between river discharge and the required time to reach downstream stations. Significant fluctuations in the river's daily flow were evident before the advent of the era of human activities, i.e., before 1992. This phenomenon persisted throughout subsequent periods, including the growth period from 1992 to 2009 and the mega-dam period spanning from 2010 to 2020, with minimal variation in the frequency of events. Sub-basins were found to significantly contribute to mainstream discharge- a contribution which led to a significant contribution of sub-basins into mainstream daily large river flow shifts. The outcomes and model derived from the sub-basin approach hold significant potential for managing river fluctuations and have broader applicability beyond the specific basin studied.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".