Joo et al. (2025) Analysis of Application of Design Standards for Future Climate Change Adaptive Agricultural Reservoirs Using Cluster Analysis

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-05
• Authors: Dong-Hyuk Joo, 라 나, Ha Young Kim, Seung-Hwan Yoo, Sang-Hyun Lee
• DOI: 10.3390/w17243463

Research Groups

Not specified in the provided text.

Short Summary

This study aimed to classify meteorologically homogeneous regions to assess climate change impact and vulnerability, identifying the Gaussian Mixture Model (GMM) as the optimal clustering method. The research determined optimal cluster numbers (k=4 or k=5) based on effective storage capacity, and subsequently identified standard reservoir designs for agricultural infrastructure.

Objective

• To assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions.
• To determine the optimal clustering method (between K-means and Gaussian Mixture Model) using the effective storage capacity to watershed area ratio.
• To identify standard reservoirs applicable to agricultural production infrastructure design standards based on homogeneous weather region clusters, the optimal clustering method, and centroid results.

Study Configuration

• Spatial Scale: Regional (implied, specific region not provided), based on 26 clusters of meteorologically homogeneous regions.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: K-means clustering, Gaussian Mixture Model (GMM) clustering.
• Data sources: Derived metric: effective storage capacity to watershed area ratio. Raw data sources for this metric were not specified. Evaluation metrics used for clustering optimization included Silhouette Score, Calinski-Harabasz Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC).

Main Results

• Gaussian Mixture Model (GMM) clustering was identified as the optimal method for classifying meteorologically homogeneous regions.
• The best clustering results for GMM were obtained at k = 4 for an effective storage capacity ranging from 100,000,000 kilograms to 400,000,000 kilograms.
• The best clustering results for GMM were obtained at k = 5 for an effective storage capacity ranging from 400,000,000 kilograms to 10,000,000,000 kilograms.
• Standard reservoirs applicable to agricultural production infrastructure design standards were identified based on the homogeneous weather region clusters, the optimal GMM clustering method, and centroid results.

Contributions

• Provides a validated optimal methodology (GMM clustering) for classifying meteorologically homogeneous regions, crucial for climate change impact and vulnerability assessments.
• Offers fundamental data and specific recommendations for the development and revision of design standards for climate-resilient agricultural infrastructure.

Funding

Not specified in the provided text.

Citation

@article{Joo2025Analysis,
  author = {Joo, Dong-Hyuk and 나, 라 and Kim, Ha Young and Yoo, Seung-Hwan and Lee, Sang-Hyun},
  title = {Analysis of Application of Design Standards for Future Climate Change Adaptive Agricultural Reservoirs Using Cluster Analysis},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17243463},
  url = {https://doi.org/10.3390/w17243463}
}