Paper Details

Drought is characterised by a negative water balance originating from a deficiency of precipitation or a lack of available water resources for an extended period of time. However, drought patterns have become more complicated in recent years due to climate change, and thus there is a need to better understand drought patterns and characteristics. Currently, the main limitations of drought analysis is the lack of ability to classify spatial pattern according to its kind and concomitant regional characteristics. This ability is increasingly important because the effects of drought accumulate slowly over a considerable period of time, and move slowly to adjacent positions. Myriads of scholars have considered clustering techniques as the most common approaches.Findings here unveiled the shortcomings and strength of composite clustering algorithms. It is clear here, that the choice of the cluster algorithm is relative subjective, yet should be bore in mind, that is there is need to explore more than one cluster algorithms in fear of losing microscopic precipitation fact and for the ease of analysis of Spatio temporal phenomena. In recognition of hydrological time series characteristics of trans boundary interference and spatio-temporal variation, where zones or regions that share common boundary may inherit similar hydro-climatic characteristics that seem different from other part of the region. It is imperative, for hydro-climatic researchers to adopt clustering algorithm that reveals the degree of shared characteristics or membership properties, in this hierarchy that is; FCM, PCA,k-means and SOM, for effective water resource planning and management in the phase of climate change. Keywords: drought, cluster, water, homogenous and region