Modelling Dekadal Rainfall Dynamics in Kenyan Subnational Regions Using Sarima Model

Kenya is very weather sensitive, and the seasonality and variability of rainfall has a significant impact on agricultural productivity, water resource management and food security. Therefore, precise rainfall prediction is a key requirement for climate risk management and decision-making. Most Kenyan rainfall studies have been of monthly and/or annual rainfall, which may not be sensitive enough to the intra-seasonal variability in rainfall seen at dekadal (10-day) time scales. The aim of this study was to model and forecast dekadal rainfall dynamics in selected Sub-national regions of Kenya using Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The quantitative time series research design adopted involved Box–Jenkins methodology with data on dekadal rainfall from 2021 to 2025 from the Humanitarian Data Exchange (HDX). The five regions namely Manyatta, Mbeere North, Igembe Central, Marsabit and Isiolo were chosen for analysis. Data was analyzed by R statistical software. Logarithmic transformation and differencing were used to stabilize the variance and to make the data stationary as verified by the Augmented Dickey–Fuller test. Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) were used to identify the candidate SARIMA models, while Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to select the models. Model adequacy was checked with residual diagnostics and Ljung–Box test, and the forecasting performance was judged based on RMSE, MAE, MAPE, and MASE. The results showed that SARIMA (1,0,0) (1,1,1) 36 was the most suitable model for regions51348,51352, and51364, whereas SARIMA (1,0,0) (0,1,1) 36 provided the best fit for regions 51357 and 51363. Residual diagnostics also showed that the models estimated were appropriate to represent the temporal dependence in the rainfall series, as the residuals were in the form of white noise. High MAPE values were observed, which were mostly related to the intermittent nature of the rainfall data, but satisfactory forecasting performance was shown by RMSE, MAE and MASE. Forecasts for the coming year reproduced the observed seasonal rainfall pattern and showed greater uncertainty in the longer-term forecasts. In general, the chosen SARIMA models were successful in modelling dekadal rainfall patterns and can be a valuable tool for agricultural planning, water resource management, and climate risk preparedness for Kenya.