Seasonal rainfall variability, drought characterisation and efficacy of interpolation techniques: Case of Kenya’s central highlands

Drier parts Eastern Kenya, endure persistent crop failure and declining agricultural productivity which have been attributed, in part, to high temperatures, prolonged dry-spells and particularly erratic rainfall. Understanding spatial-temporal rainfall variability is a key facet to agricultural productivity and Natural Resource Management (NRM) in the region. However, meteorological stations in the region are sole sources of climatic data but only limited to single locations, far away from each other, with inadequate or inconsistent and missing meteorological data. This study examined the extent of rainfall variability and the efficacy of interpolation techniques in the arid regions, east of Kenya’s central highlands. Rainfall raw data gaps were filled using multiple imputations. Cumulative Departure Index (CDI), Rainfall Anomaly Index (RAI) and Coefficients-of-Variance (CV) and probabilistic statistics were utilised in the analyses of rainfall variability. Evaluation of interpolation techniques utilized the ArcGIS environmental tool combined with the digital elevation model (DEM) to generate spatial rainfall maps under Spline, Kriging and Inverse Distance Weighting (IDW) techniques whose efficacy was evaluated using root mean square errors (RMSE), mean absolute errors (MAE) statistics and gauged-data validation. Analyses showed 90% chance of below cropping-threshold rainfall (500 mm) exceeding 258.1mm (Embu) during short rains (SRs) for one year return-period. Rainfall variability was found to be high in seasonal amounts (CV=0.56) and in number of rainy-days (CV=0.88). Monthly rainfall variability was found to be high during peak (April and November) (CV=0.48 and 0.76) with high probabilities (0.67) of droughts exceeding 15 days. Dry-spell probabilities within growing months were high (81%) and (60%) in Machang’a and Embu respectively. Kriging technique emerged as the most appropriate Geo-statistical interpolation techniques to be used in the region. To optimize yield in the area, use of soil-water conservation and supplementary irrigation, crop selection and timely accurate rainfall forecasting should be prioritized.