Wavelet-entropy enhanced clustering: a comprehensive analysis of drought patterns in the Southern Plains, USA

Authors: LEE, SANGHYUN - US Department Of Agriculture (USDA); VAHID, MOURANI - University Of Tabriz; DANANDEH MEHR, ALI - Antalya Bilim University; Moriasi, Daniel; MIRCHI, ALI - Oklahoma State University

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/24/2024
Publication Date: 12/1/2024
Citation: Lee, S., Vahid, M., Danandeh Mehr, A., Moriasi, D.N., Mirchi, A. 2024. Wavelet-entropy enhanced clustering: a comprehensive analysis of drought patterns in the Southern Plains, USA. Journal of Hydrometeorology. 25(12):1809-1822. https://doi.org/10.1175/JHM-D-24-0041.1.
DOI: https://doi.org/10.1175/JHM-D-24-0041.1

Interpretive Summary: Scientists investigated differences in drought patterns over time in the Southern Plains (SP; Kansas, Oklahoma, and Texas) region. To manage drought effectively, similar areas are grouped together. Past methods used only summer precipitation (P) to group similar drought areas together, while drought can be affected by other variables such as evapotranspiration (ET; computed based on temperature, relative humidity, wind speed) and land cover or vegetation. In this study, a new innovative approach that utilizes multiple drought indicators was used to group similar drought-pattern areas in the SP to create a drought cluster map. Scientists evaluated various combinations of P, ET, and vegetation as primary drought indicators and geographic location and elevation as secondary indicators. The grouping of similar drought areas based on P, ET, and vegetation indicators as well geographic location and elevation was best and resulted in the SP drought cluster map consisting of twenty areas. This new approach shows promise to create a reliable drought cluster map for understanding and addressing drought dynamics in the SP region. The USDA is an equal opportunity provider and employer.

Technical Abstract: Droughts may exhibit spatiotemporal heterogeneity at 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 risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal 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 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 twenty clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.80 to 0.93. Comparison with an existing cluster map (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. The USDA is an equal opportunity provider and employer.