Analysis of Climate Variability and Change Impact on Rainfall Trend Pattern in Nigeria
Bibliographic record
Abstract
The climate variability and change impact on rainfall trend pattern in Nigeria in general and Calabar river basin in particular were analyzed in the present study. It involved analysis of climatic data documentation of the Nigerian Meteorological Agency (NIMET), Calabar Station that span 43 years (1971–2014) of the study area and compared against NIMET’s historical meteorological maps of Nigeria between 1941–1970 and 1971–2000, otherwise called the base period to ascertain the status of climate variability and change. The comparison revealed a historical sequential rise in temperature, evidenced by late onset and early cessation of the rains. The late onset and early cessation of rains have necessitated the contraction of the length of the rainy season. This has impacted negatively on farming practices in the region. Furthermore, there is also evidence of significant changes in known weather patterns in the region. For example, the little dry season, then commonly known as August Break, has become less significant in the region. Similarly, the analysis has revealed that the environment has become warmer as temperatures have risen considerably and Harmattan dust haze has also become more pronounced in recent years. The evaluation of climate change pattern of Calabar river basin was necessitated owing to its distinct socio-economic benefit to Nigeria. The region plays host to Africa’s foremost leisure resort (Tinapa), one of world’s largest rubber plantations (Pamol), the National Integrated Power Project (NIPP), Nigerian Police Training School, etc. In Nigeria, Cross River State has over 40% of the remaining tropical high forests (THFs) of the entire nation. The forest resource base includes the mangrove swamps and tropical rainforest in the south, the central, and the derived Guinea savanna toward the north. Thus, Cross River has become the most forested State in Nigeria with at least 75% of its population inhabiting rural communities. However, over the last decades, the region has lost about 19% of its tropical high forests due to inadequate funding of the Forestry Department, increase in population and immigration, and plantation establishment. To ameliorate the effects of climate change, the study recommends improved energy efficiency, shift to renewable resources/cleaner source of energy (solar and wind), reduced deforestation and planting of trees. Furthermore, there should be absolute compliance with international organizations’ action plans that fight global warming, i.e., the Montreal Protocol of 1987, the 1979 convention on long-range trans-boundary air pollution, the Kyoto Protocol of 1997, the clean air act (1990), UNCED (1992), USA (2008) congress, etc.
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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 | 0.002 |
| 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".