Impacts of climate change on precipitation phase trends in the Upper Langtang glacier river basin
Bibliographic record
Abstract
Mountain studies worldwide have documented increases in rainfall fraction as an impact of climate change. Most mountain systems show an increasing trend in rainfall fraction due to shifting snow precipitation to rain. In Nepal, which occupies an 800 km-long belt of the Hindu Kush Himalaya, the precipitation phase trend is not well known. This study conducts a precipitation phase study in the Langtang region and investigates the response of snowfall and rainfall to recent climate variation and change. The study uses 40 years (1979-2018) of bias-corrected WFDEI climate reanalysis data and applies a physically based psychrometric energy-balance model to partition precipitation into rainfall and snowfall. The study identifies points of statistically significant changes in trends using the Exponential Weighted Average, Mann-Kendall test, Sen’s slope estimator, and changepoint analysis. Changepoint detection was conducted using Pettitt’s method. In addition, the study estimates transient temperature for rainfall-snowfall transition using logistic mapping. The results show that the rainfall fraction has been increasing for all timestamps (annual, seasonal, monthly) except winter nights, with changepoints occurring between 1990-2000. Post-monsoon months (October and November) showed the greatest annual increase in daytime rainfall fraction at 0.34% and 0.27%, respectively. Winter months exhibited the least changes, with no significant increase in rainfall, particularly at night. The transient temperature for rainfall-snowfall transition was identified at 1.78°C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".