Analysis of Rainfall Distribution, Temporal Trends, and Rates of Change in the Savannah Zones of Nigeria
Bibliographic record
Abstract
The impact of climate change is often demonstrated by rainfall and its attributes. Consequently, this study analyzes rainfall concentration, temporal trends, and rates of change in the savannah zones of Nigeria. Rainfall data were acquired from the archives of the Environmental Management Programme, Federal University of Technology, Minna, for 13 synoptic stations at annual, seasonal, and monthly time scales for the 1970–2016 period. The precipitation concentration index (PCI), Mann–Kendall trend test, Theil–Sen’s slope estimator (β), and relative percentage change methods were adopted for data analysis. The findings reveal that PCI calculated on an annual scale falls into three categories 11–15, 16–20, and PCI > 20. Two distinct patterns emerged from the calculated PCI indicating that stations in the Guinea savannah zone (Bida, Yola, Minna, Jos, Bauchi, and Kaduna) have moderate, irregular, and strongly irregular rainfall concentrations, whereas stations in the Sudano-Sahelian savannah zone (Kano, Gusau, Maiduguri, Yelwa, Nguru, Sokoto, and Katsina) have irregular and strongly irregular rainfall concentrations. The Mann–Kendall analysis of the PCI values reveals that 8 of the 13 stations (62%) experienced downward trends. This implies that rainfall is sliding toward a moderate to uniform distribution. The trends, and consequently the variability in the annual and seasonal rainfall, reveal that, with the exception of Yola and Jos stations, where the trends were downward, the overall rainfall was increasing significantly in some areas and insignificantly in others. The magnitude of the significant upward trends in the annual rainfall was found to be 3.59 mm yr−1 at Yelwa station, 9.84 mm yr−1 at Bauchi station, 17.13 mm yr−1 at Kano station, 3.98 mm yr−1 at Sokoto station, and 3.11 mm yr−1 at Katsina station. It is understood that the changes in rainfall distribution and trends have positive effects on water availability for crops, and this should facilitate enhanced productivity in rain-fed farming.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".