Climate change and probabilistic scenario of streamflow extremes in an alpine region
Bibliographic record
Abstract
Abstract Future projections of streamflow extremes are of paramount significance in assessing the climate impacts on social and natural systems, particularly for the Himalayan alpine region in the Tibetan Plateau known as the Asian water tower. This study strives to quantify the uncertainties from different sources in simulating future extreme flows and seeks to construct reliable scenarios of future extreme flows for the headwater catchment of the Yellow River Basin in the 21st century. The results can be formulated as follows: (1) The revised snow model based on a daily active temperature method is superior to the commonly used degree‐day method in simulating snowmelt processes. (2) In general, hydrological models contribute more uncertainties than the downscaling methods in high flow and low flow over the cryospheric alpine regions characterized by the snow‐rainfall‐induced runoff processes under most scenarios. Meanwhile, impacts to uncertainty vary with time. (3) The ultimate probability of high flow exhibits a downward trend in future by using an unconditional method, whereas positive changes in the probability of low flow are projected. The method in the work includes a variety of influence from different contributing factors (e.g., downscaling models, hydrological models, model parameters, and their simulation skills) on streamflow projection, therefore can offer more information (i.e., different percentiles of flow and uncertainty ranges) for future water resource planning compared with the purely deterministic approaches. Hence, the results are beneficial to boost our current methodologies of climate impact research in the Himalayan alpine zone.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".