Assessment of Global and Regional Reanalyses Data for Hydro- Climatic Impact Studies in the Upper Thames River Basin
Bibliographic record
Abstract
This study evaluates NCEP-NCAR reanalyses hydro-climatic data as an initial check for assessment of climate change studies and hydrologic modeling on the basin scale. Reanalysis data set for daily precipitation, and temperature from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) (a) global (NNGR) and (b) regional (NARR) reanalysis project are used as input into the semi-distributed hydrologic model (HEC-HMS) during the period of 1980-2005. First, the precipitation and temperature data are interpolated to selected stations to check for their trends and similarity in means and variances. Although NARR shows some over-estimated values, mainly in estimating temperature during the summer months, it has been able to capture the trends. NNGR, on the other hand, has produced inferior results in many cases, especially in generating precipitation when compared with the observed values. With its improved atmospheric analytical ability, NARR appears to have performed better than the NNGR, suggesting that with coarse resolution NNGR may not be applied in climate change studies for medium or small watersheds. Next, an extensive analysis is performed for assessing the performance of the reanalysis data generated flows by comparing it with the observed inputs during May-November. The stream flows generated from the NARR dataset show encouraging results for simulating summertime low flows with less variability and error. NNGR dataset, have proven to be less accurate and highly variable. This study suggests that NARR can be adequately used as either an additional source of data or as an alternative to observations in data scarce regions.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".