Vulnerability of Calabar Rainfall to Climatic Variability Events: A Critical Factor in Integrated Water Resources Management in the Tropical Coastal Location in Southeastern Nigeria
Bibliographic record
Abstract
In this study, Calabar annual total rainfall was analysed for trend and climatic variability events with focus on drought occurrence. Monthly rainfall data from in situ measurements over a 41 year period (1972-2012) were used for the study. Standard tests were used to evaluate the trends and variability in annual rainfall. Rainfall variability was estimated as standardized rainfall departures and used to identify and delimitate the dry and wet spell sequences of Calabar rainfall. The rainfall series were analysed for Standardized Precipitation Index (SPI) using SPSS Version 17 software. The least squares regression plot was executed using Excel 2010 to depict the trend, variability and regression parameters. The average annual rainfall for Calabar is 2984.64 mm with standard deviation of 394.9 mm. 36.59% of the period showed positive SPI while 63.41 % of the period indicated negative SPI values. Wet spell dominated the later part of the period but sandwiched with dry spells whereas dry spell were dominant from 1972 to 1994. The area experienced a non-significant upward trend of 15.21 mm per year over the interval. The SPI values indicate that Calabar experienced distinct inter-annual rainfall cycles that represent mild to extreme droughts and wet spells which are a demonstration of consequential annual rainfall variability. The results underscore the need for effective monitoring of Calabar rainfall for prompt warnings and responses that would guarantee effective risk reduction and management in the run-in to the occurrence of the extreme events. The paper further highlights the need for data-driven approach to policy making in water resources management. This would provide a fascinating insight into the improvement in long-term water resources management in the city and its suburbs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".