Trend analysis of statistically downscaled precipitation for tropical semi-arid climate
Bibliographic record
Abstract
The coarse resolution climatic data extracted from the global climate models (GCM) cannot be utilised straightaway for research works on climate change impact analysis. Thus, the downscaling technique is used to attain a higher resolution scenario from the GCM. In the current study, the Canadian Centre for Climate Modelling and Analysis (CCCma)-GCM is used to predict monthly precipitation using the statistical downscaling technique for the tropical semi-arid region of Eastern Gujarat for the period 2019-2099. Geo-potential height (h500) and mean sea level pressure (MSLP) are chosen as explanatory variables for the downscaling model. The model is developed using the principal component analysis (PCA) - multiple linear regression (MLR) combined approach. The model is applied to predict rainfall for three representative concentration pathway (RCP) scenarios, RCP2.6, RCP 4.5, and RCP 8.5. The best-suited scenario for the study area is selected using robust statistical indicators such as the Nash-Sutcliffe efficiency and the root mean square error. Several parametric and nonparametric tests are conducted to analyse rainfall trends in the Eastern Gujarat region. The outcomes showed that the precipitation scenario produced for RCP4.5 replicates the climatology of the region suitably. The trend investigation of the predicted rainfall showed that the significance of the seasonal trend is independent of the significance of monthly trends. Trend analysis of downscaled precipitation series can report multiple change points for a region. Further, the annual rainfall increases tremendously in the tropical semi-arid regions over the 21st century. This study will provide insights into sustainable water resource management and development.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".