Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, United States

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Abstract Droughts may exhibit spatiotemporal heterogeneity at a regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought clustering map for the southern plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatiotemporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and standardized precipitation evapotranspiration index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on the Davies–Bouldin and Calinski–Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 and 0.94. Comparison with an existing cluster map [Drought Risk Atlas (DRA)] for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.