| Summary: | Rainfall is a complex meteorological phenomenon with high spatio-temporal variability. These variations in hydrologic time series can be better analyzed using homogeneity and trend tests. In this paper, three homogeneity tests were applied to the annual rainfall series of 1980–2013 from 30 raingauge stations over the study area. The rainfall stations with homogeneous time series were subjected to cluster analysis (Agglomerative Hierarchical Clustering technique) and thereafter traditional trend tests (Mann-Kendall test and Spearman Rank-order Correlation test) and one emerging trend test (called ‘Innovative Trend test’) were applied to individual clusters. Based on the homogeneity test results, 27 rainfall stations with homogeneous time series were classified under six clusters. The results of the trend analysis revealed that a distinct pattern of increasing (decreasing) rainfall trend exists in the northern (southern) part of the study area. Although the traditional tests detected insignificant trends, the Innovative Trend (IT) test indicated the trends to be significant in all the clusters. However, the magnitude of trends detected by the traditional trend tests and non-traditional trend tests is similar for all the clusters. It is concluded that the emerging IT test is highly sensitive to the changes in the rainfall time series. The findings of this study suggest that the orography, population density, industrialization and agricultural intensity lead to a precipitation gradient in the study area.
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