Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches

The knowledge of the climate pattern for a particular region is important taking appropriate actions to alleviate impact change. It also equally water resource planning and management purposes. In this study, regional disparities similarities have been revealed among different stations in Bangladesh based on an adaptive clustering algorithms that include hierarchical clustering, partitioning around medoids, k-means techniques under several validation measures climatological factors including rainfall, maximum temperatures, wind speed. \(H_{1}\) statistics L-moment method were used test homogeneity identified clusters by algorithms. results suggest can be grouped into two prime clusters. most cases, one cluster located northern part country includes drought-prone vulnerable regions, whereas, second contains rain-prone hilly regions are found mostly southern part. terms size homogeneity, all identified. contrast, three either homogeneous or reasonably homogeneous. implementation principal component analysis station data further reveals latent play vital role address total variations.