Assessment of Rainfall and Temperature Trend in Zaria and its Environs, Kaduna State, Nigeria
DOI:
https://doi.org/10.56892/bima.v8i2.668Keywords:
Rainfall, temperatures, descriptive statistics, coefficient of variability annual and decadal variabilityAbstract
Trend assessment particularly in rainfall and temperature is one of the keys for understanding climate change in a given geographical area. Zaria and its’ environs located in Northern Kaduna State, Nigeria, characterized as a region of high climatic variability, which may have changed the trend in rainfall and temperature (maximum, minimum and average). Objectives of the study are to determine variability and evaluate trends in rainfall and temperatures. Rainfall and temperature data for 30 years (1991-2020) was analyzed using Coefficient of Variability (CV), descriptive statistics, annual and decadal variability. The mean rainfall (1089.216mm), maximum (32.62), minimum (19.14) and average temperature (25.86) as the climate normal for the respective variables in 30 years. The 30 years (1991-2020) was divided into three decades (1991-2000, 2001-2010 and 2011-2020) to obtain mean for each decade. The mean of each decade was compared to climate normal to obtain decadal variability and percentage change. Result showed annual variation in all the variables with coefficient of variability (CV) of 15.8%, 5.3%, 6.7% and 5.1% in rainfall, maximum, minimum and average temperature respectively. Decadal variability of the three decades, rainfall revealed changes of -11.2%, 2.5% and 8.7%. Maximum temperature revealed -5.4%, 1.5% and 4.1%, minimum revealed -5.7%, 2.8% and 2.8% while average temperature showed -5.4%, 1.8% and 3.8% respectively. Positive values of rainfall indicated surplus (wet) decade while negative values indicated deficit (dry). The hottest decades in maximum, minimum and average temperatures show positive values while least hot (cool) decades show negative values. Kaduna state government should continue to provide early warning system for communities. This may help forecast the occurrence of floods.