Abstract:
With its diverse climates and ecosystems, Central Oromia encompasses the Peak
Mountains, plateaus, highlands, plains, lakes, rivers and the Great African Rift Valley. The
study area had been affected by seasonal rainfall variability that lead to shorter rainy period,
excess and deficit rainfall, and weather disasters resulting to drastically threatened
agricultural, water resources and socio-economic activities during anomalous years. This
study focused on characterizing rainy season, evaluating variability, and examining
predictability of the rainy season. The study important for generating scientific findings,
which is essential for effective water resource management, agricultural planning, and
disaster preparedness. Forty years of stations’ data from 1981–2020, gridded rainfall, and
global data were utilized. Statistical analysis to characterize and identify rainfall variability,
Correlation and composite analysis to test predictability, multiple linear regression, and
linear discriminant analysis techniques to develop seasonal forecasting model applied. The
forecasting model skill assessed using relative operating characteristics and a ranked
probabilistic skill score. The study area is classified into four homogeneous rainfall
regimes, namely Regime I, Regime II, Rime III, and Regime IV, based on the rainfall
annual cycle and inter-annual variability. Seasonal rainfall characteristics show early onset
over west parts and late onset over Great Rift Valley, and the reverse is true for cessation.
The length of the growing period is longer over west parts but very shorter over low lands.
The greater variability noted and a strong irregular rainfall distribution over RII. The
Atlantic, Nino 3.4, and West Indian Ocean had a significance influence on Regimes I and
IV; however, the West Indian and Atlantic had on Regimes II and III during main rainy
season. For second rainy season Regimes I and II by Nino-3.4, and West Indian Ocean,
whereas Regimes III and IV by the Atlantic, Nino-3.4, and the West Indian Ocean. Models
show different forecasting skill for each regime. The study suggests that in order to
understand the dynamics of climate variability, future research should concentrate on the
elements causing unseasonal rainfall events during the dry season. In order to close the gap
in rainfall characteristics, current variability, and notable predictability at the local level,
this finding should prompted to the central Oromia.
