Markov Chain Analysis of Yearly Precipitation Amount Over the South-South Region in Nigeria
AGADA, I.O. *
Department of Physics, Joseph Sarwuan Tarka University, Makurdi Benue State, Nigeria.
AUDU, M.O.
Department of Physics, Joseph Sarwuan Tarka University, Makurdi Benue State, Nigeria.
ADAH, V.
Department of Statistics, Joseph Sarwuan Tarka University, Makurdi. Benue State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Aims: Markov chain analysis was employed to examine the pattern and distribution of yearly precipitation amount in Akwa Ibom, Bayelsa, Rivers, Cross River, Edo and Delta states in Nigeria.
Duration of Study: Twenty-one (21) years data (1990-2020) on daily precipitation amount was obtained from National Aeronautic and Space Administration (NASA) metrological center.
Methods: The standardized anomalies and Markov Chain method was employed in this study. The outcome of the standardized anomalies was used to rank yearly precipitation amount into different Markov Chain states for ease analysis. A seven-state (1: Wet, 2: Moderately wet, 3: Slightly wet, 4: Near normal, 5: Slightly dry, 6: Moderately dry and 7: Dry).
Results: A Markov chain was used to describe the behavior of precipitation occurrences in the study locations. Findings revealed that Rivers state had the highest amount of precipitation in 2007, while Edo had the lowest amount in 1999 over the study period. The standardized anomalies shows major positive departure in Rivers compared to other regions in the South-South. There is a 48%, 33%, 38%, 33%, 35% and 38% chance of precipitation amount been normal (state 4) on any given year regardless of previous weather conditions in Akwa Ibom, Bayelsa, Edo, Rivers, Cross river and Delta respectively.
Conclusion: Understanding the transition from one Markov chain state of precipitation amount to another state is necessary for future planning in areas like agriculture, hydrological studies and the entire planning of the South-South region.
Keywords: Markov chain, standardized anomalies, rivers, Nigeria, precipitation amount