Climate Change Impacts on Extreme Flow Measures in Satluj River Basin in India
Bibliographic record
Abstract
A major impact of climate change is likely on the frequency and magnitude of extreme flow events. Hydrological systems have traditionally been designed on the assumption that the available flow records for a location reflect stationary climatic conditions. In view of the recent climate change, the assumption of stationarity in the flow records cannot be justified. Design of hydrological systems is, therefore, likely to be more reliable if the impacts of potential climate change on extreme events are considered. This paper investigates trends in extreme flow measures for a set of streamflow gauging stations in Satluj River Basin in India. Linkages of extreme flow measures with large scale climate indices have also been identified. The analysis includes an exploration of the types of trends that may occur in an extreme flow record, which include changes in the timing of extreme events, and changes in the extreme event magnitudes. Several extreme flow measures including the high flow and low flow magnitudes and their dates of occurrence have been analyzed for the detection of trends using Mann-Kendall non parametric test. The results reveal more trends than would be expected to occur by chance for various measures of extreme flow characteristics. The data has been found to exhibit changes in both the magnitude and the timing of extreme flow events. Analysis of extreme flow measures presented herein is likely to lend credibility to recent climate change modeling efforts, and would help detect climate change impacts on hydrological regime.
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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 | 0.001 | 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) | 0.000 | 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".