Climate Change and Trend Detection in Selected Rivers within the Asia-Pacific Region
Bibliographic record
Abstract
Global climate change is currently an issue of great concern. This phenomenon was studied using the runoff of large rivers, which can be considered a regional integrator of the local precipitation occurring in their basins. The long-term stationarity and the possibility of trends in streamflow records stored in the databank of the Global Runoff Data Center (GRDC) at the Federal Institute of Hydrology in Koblenz (Germany) were studied. Runoff records originating from 78 rivers with long monthly runoff series that are geographically distributed throughout the whole Asia-Pacific region were selected for study. For each of the selected rivers, three time series were constructed and analyzed: the mean yearly, and the maximum and minimum monthly discharges. These series were submitted to a two-tier analysis. First, a segmentation procedure developed by Hubert was applied to assess their stationarity. Then the segmented series were submitted to a specialized trend detection software. The results show that about two-thirds of the series have remained stationary and that the monthly minimum runoff exhibited more changing levels (37/78) than the mean (26/78) and maximum (18/78) runoff. Most of the detected changes occurred during the 1960s and 1970s, a period of rapid demographic expansion and urbanization in Asia, when irrigation and other water uses were developed, especially in tropical areas. During the same period and within the area studied, a number of large dams and reservoirs were completed. Since these anthropic interventions could be at the origin of the changes in runoff, there is no regionally consistent evidence supporting global climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".