Teshome Leta*
Rainfall is the most significant element of weather and climate that directly affects agriculture, water resources, and more importantly, the socioeconomic situation of the society. Rainfall is a prime input for various engineering design such as hydraulic structures, bridges and culverts, canals, storm water sewer and road drainage system. The impacts of climate change and climate variability on human life have led the scientific community to monitor the behavior of weather and climate variables. Rainfall as one of the most important of these variables has a direct and indirect impact on the natural environment and human life. A large spatial and temporal variability of rainfall leads to an increased incidence of extreme events such as floods and droughts. The detailed statistical analysis of each region is essential to estimate the relevant input value for design and analysis of engineering structures and also for crop planning. This paper has examined the annual and seasonal rainfall variability and trends in the Metekele zone mandura, Bullen, Pawe, Wombera, Guba, Dangur and Dibate weredas, North Western Ethiopia.
The rainfall variability and trends over Metekele zone investigated using gauge as well as gridded rainfall data from 1989 to 2014. The variability of rainfall in both annual and seasonal was evaluated using Coefficient of Variation (CV), standardized rainfall anomaly. Monthly rainfall data were obtained from the Ethiopia Meteorological Institute (EMI) of Ethiopia. Following data quality checking and inspection, the monthly rainfall data of the selected stations were arranged in Microsoft excel spread sheet and analyzed using XLSTAT software. Mean, standard deviation, coefficient of variation, Sen’s Slope Estimator (SEE) and the Mann Kendall (MK) non-parametric statistical test was applied to study trends and variability of rainfall in Metekele zone. To assess the spatial extent of rainfall in the study area, trend time series values of each meteorological station have been interpolated by Inverse Distance Weighted (IDW) method using the spatial analyst tool of ArcGIS. The IDW method gives better representation for interpolation of rainfall distribution. The Inverse Weighting Distance (IDW) interpolation technique was employed to generate surface data and produce spatial rainfall maps.