On the cause of large daily river flow fluctuations in the Mekong River
Bibliographic record
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 Lancang–Mekong River (hereafter LMR), 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 stations. Here, we address this with a newly developed subbasin modeling framework that integrates 3D hydrodynamic and response time models, together with a hydrological model with an embedded reservoir module. This integration allows us to estimate the time required between two hydrological stations and to distinguish between the contributions of subbasins and upstream regions to large daily river flow alterations. The findings revealed a power correlation between upstream river discharge and the time required to reach downstream stations. Significant fluctuations (greater than 1 m) in the river's daily flow were evident before the advent of the era of human activities, i.e., before 1992, with around 92 % of these fluctuations occurring during the wet season, particularly in June, July, and August. This pattern persisted throughout subsequent periods, including the growth period (1992–2009) and the mega-dam period (2010 to 2020), with minimal variation in the frequency of events. The Lancang basin contributed approximately 33 %–42 % of these large river fluctuations at the Chiang Saen station. We found that daily-scale water level and runoff might not fully capture dynamic river flow changes, as significant differences were observed between daily and subdaily river flow profiles. Subbasins significantly contributed to mainstream discharge, leading to substantial shifts in mainstream daily river flows. The outcomes and model derived from the subbasin approach have significant potential for managing river fluctuations and 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".