IMPACTS OF SNOWMELT AND CLIMATE CHANGE ON HIMALAYAN RIVERS: THE CASE OF DATA-SCARCE DUDHKOSHI RIVER IN EASTERN NEPAL
Bibliographic record
Abstract
Water from snow-melt is crucial to provide ecosystem services in downstream of the Himalayas. To study the fate of snow hydrology, an integrated modeling system has been developed coupling Statistical Downscaling Model (SDSM) outputs with Snowmelt Runoff Model (SRM) in the Dudhkoshi Basin, Nepal. The SRM model is well-calibrated in 2011 and validated in 2012 and 2014 using MODIS satellite data. The annual average observed and simulated discharges for the calibration year are 177.89 m3 /s and 181.47 m3 /s respectively. To assess future climate projections for the periods 2020s, 2050s, and 2080s, the SDSM model is used for downscaling precipitation, maximum temperature, and minimum temperature from the Canadian GCM model (CanESM2) under three different scenarios RCP2.6, RCP4.5 and RCP8.5. All considered scenarios are significant in predicting increasing trends of maximumminimum temperature and precipitation and the storehouse of freshwater in the mountains is expected to deplete rapidly if global warming continues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".