Assessment of Suitability of Gridded Precipitation Data for Hydrological Simulation in Eastern Himalaya: A Case Study
Bibliographic record
Abstract
Gridded precipitation datasets have been effectively employed in hydrological modeling in absence of gauge data. The study assessed the applicability of five spatially distributed precipitation datasets, Indian Meteorological Department [IMD] (gauge-interpolated), Climate Forecast System Reanalysis [CFSR] (reanalysis), Tropical Rainfall Measuring Mission [TRMM] (satellite-based), Precipitation Estimation From Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR] (satellite-based), and Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources [APHRODITE] (gauge-interpolated), for hydrological modeling in an Eastern Himalayan basin. These gridded datasets were input to the Soil and Water Assessment Tool (SWAT), which was calibrated using the SWAT-CUP SUFI2 algorithm. Based on monthly simulated results, the CFSR gridded dataset outperformed others. Streamflow underprediction was also acceptable for the entire study period. IMD and TRMM performed satisfactorily in calibration but failed to perform in validation. APHRODITE and PERSIANN showed good correlation, but due to the overall low rainfall estimation, the data failed to produce satisfactory results and hence is considered unsuitable for hydrological simulation. The TRMM model simulation had the best overall trend against the observed data but failed to match the peaks. The study concluded that CFSR can be alternatively used for modeling in the absence of gauge data for the mountainous river basins of Eastern Himalaya.
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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 | 0.002 | 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 | 0.001 |
| 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".