Analysis of Application of Design Standards for Future Climate Change Adaptive Agricultural Reservoirs Using Cluster Analysis

This study aimed to assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions. To determine the optimal clustering method, both K-means and Gaussian Mixture Model (GMM) clustering were analyzed using the effective storage capacity to watershed area ratio. The optimal number of clusters was derived based on several evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Ultimately, GMM clustering was identified as the optimal method, with the best clustering results obtained at k = 4 for an effective storage capacity of 100,000 to 400,000 tons and k = 5 for an effective storage capacity of 400,000 to 10,000,000 tons. Additionally, standard reservoirs applicable to agricultural production infrastructure design standards were identified based on homogeneous weather region clusters, the optimal clustering method, and centroid results. The findings of this study can serve as fundamental data for the development and revision of design standards, contributing to more climate-resilient agricultural infrastructure.