Abstract
The lack of truly reliable data for climate change analyses and prediction presents challenges in climate modeling. Needed data are required to be hydrologically/statistically reliable to be useful for hydrological, meteorological, climate change, and estimation studies. Thus, data quality and homogeneity screening are preliminary analyses. In this study, the homogeneity of the climatic data used for analyses of climate variability was conducted in the coastal region of Nigeria. Climatic Research Unit (CRU 0.5× 0.5) gridded monthly climatic data for sixty years (1956- 2016) for nine states of the coastal region of Nigeria obtained from internet sources were validated with the Nigerian Meteorological Agency (NiMet) data to assure adequacy for use. The data were tested for normality using the Shapiro-Wilk (S-W) test, D’Agostino-Pearson omnibus test, and skewness test. Four homogeneity test methods were applied to 257 locations in the nine states of the coastal region of Nigeria and they include Pettit’s, Standard Normal Homogeneity Test (SNHT), Buishand’s and Von Neumann Ratio (VNR) tests. The results of the validity analysis indicated that the CRU data are very reliable and thus justified their use for the further analysis carried out in the study. Also, the results obtained indicated that CRU climatic data series were normally distributed and parametric methods could be used in further analysis of the data. Rainfall data homogeneity was detected for Bayelsa, Delta, Edo, Lagos, Ogun, and Ondo states and inhomogeneity for Akwa Ibom, Cross Rivers, and Rivers States. Also, temperature data inhomogeneity was detected for all the states in the study area.
Highlights
Reliable and accurate estimates of climate are crucial for the study of climate variability but are important for water resource management, agriculture, weather, climate, and hydrological forecasting (Sarojini et al, 2016)
The study area is the coastal region of Nigeria
The results indicate that the three normality tests were in agreement that rainfall series follow a normal distribution
Summary
Reliable and accurate estimates of climate are crucial for the study of climate variability but are important for water resource management, agriculture, weather, climate, and hydrological forecasting (Sarojini et al, 2016). Because of the practical observational limitations, this measurement often suffers from numerous gaps in space and time, due to weather stations being limited in numbers and often unevenly distributed, resulting in missing data problem, a short period of observation, incomplete areal coverage, and deficiencies over most oceanic and sparsely populated areas (Kidd et al.,2017), making its use in climate change diagnostic studies less reliable in initial data processing and calibration problems of subjecting non-continuous rainfall and temperature data into the Water Balance and TREND software This may arise as this software often recognizes only continuous data of long duration over fifty (50) years. Mitchel and Jones (2005) recommended that a large proportion of such data needs can be met through providing a standard set of ‘climate grids’, in terms of monthly variations over a century-long time scale on a regular high-resolution (0.5°) latitude-longitude grid
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