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An Eigen-based technique for sequential spatial pattern analysis, an application of PCA (Principal component analysis), is presented here. The analysis examines the spatial distribution of precipitation in the Mahanadi River basin. The spatial (S) mode of sequential spatial pattern analysis with the application of the maximum loading value of retained rotated principal component (also referred to as the maximum loading value approach) to assess gridded monthly rainfall data with a resolution of 0.25° × 0.25° and a record length of 117 years (1901–2017). Meteorological records have a sequential spatialkfield for the spatial and temporal mode that is used to recognize the area of precipitation variability and regime. The identified patterns of the different timeslot segments were then analysed for their dispersions of the annual precipitation observed at different station points using similarities and dissimilarities characteristics of inter-cluster and between-clusters, respectively. Validation of the regionalized pattern for distinctness and pairwise comparison of CDF's using the Kolmogorov-Smirnov ‘D’ statistical test. The statistics result in 6 of 8 core origins being located within the upper and middle sub-division of the Mahanadi River basin, suggesting any variation in the basin is due to the influence of core points. Furthermore, the spatial pattern variability of summer is not observed in the middle Mahanadi, while high variability is observed in the lower Mahanadi.

Segmental variability of precipitation in the Mahanadi river basin from 1901 to 2017