Application of HBV Model in Hydrological Studies of Nepali River Basins: A Case Study
Bibliographic record
Abstract
Ungauged basins are challenges for hydrological study, the key discipline to analyse for planning and the operation of water resources projects. Several river basins have no hydrologic measurements where there is feasibility of promising water resources schemes. This study deals with use of the Hydrologiska Byråns avdeling for Vattenbalans (HBV) hydrological model to generate stream flow time series and other hydrological variables. The model was calibrated successfully in the Sanghutar catchment of the Likhu River of Nepal, and then used to simulate runoff series at the proposed intake site of Likhu HEP, where the gauging station has not been installed. The model can be used to generate runoff of other ungauged catchments which have similar catchment characteristics.DOI: http://dx.doi.org/10.3126/hn.v8i0.4910 Hydro Nepal: Journal of Water, Energy and Environment Issue No. 8, 2011 JanuaryPage: 38-43Uploaded date: 17 June, 2011
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".