Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches

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