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<p>The climatic and hydrological variables play a significant role in water resources planning and management, due to their high spatial and temporal variability. While the classification of climatic regions based on various variables is challenging, effective zoning can improve our response to climate-induced hazards like floods and droughts. This study addresses these challenges by employing the C-means clustering algorithm to classify Iran based on monthly and annual precipitation, temperature, and meteorological drought, using observational data from 71 synoptic stations and satellite products, including ERA-5 and NASA-Power, spanning the years 1987 to 2017. The findings of this study indicate that the monthly performance of the ERA-5 satellite surpasses that of NASA-Power. Specifically, the ERA-5 satellite exhibited optimal performance in September and October, achieving R² values of 0.76 and 0.70, respectively, while NASA-Power recorded its highest performance in May and June, with R² values of 0.59. Conversely, the annual precipitation performance demonstrated superior results. The classification analysis revealed that the Central Plateau of Iran constitutes the largest region, consistently classified within the 1 to 2 class range across all scenarios, encompassing 50% of the country’s total area. The findings indicated that the northwest area, characterized by a cold and frigid climate, is consistently grouped in the same category across most terrestrial and satellite scenarios, encompassing approximately 13% of Iran’s territory within this climate. In the majority of scenarios, the northern part of Iran, which experiences the highest levels of rainfall, is divided into two distinct regions, both of which occupy the smallest land area, each being less than 1% of Iran’s total expanse. All scenarios categorize Iran into 5 to 9 classes, with the 5-class scenario corresponding to monthly precipitation and SPI 3 yielding 9 classes. Based on the homogeneity results, the classification derived from monthly and annual precipitation data is identified as the most effective. Furthermore, the results suggest that the classification based on NASA-Power satellite data aligns more closely with observational classifications. Effective climate zoning can contribute to environmental sustainability by optimizing resource management and minimizing the adverse impacts of climate change. The findings of this study can be beneficial for agriculture, selecting appropriate dam sites, and effective water resource management.</p>

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A fuzzy-based approach for clustering the meteorological drought over Iran

  • 期刊论文

  • 发表日期：

    2024年12月31日

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  • Zahra Khaghani,
  • Ahmad Sharafati,
  • Yusef Kheyruri,
  • Asaad Shakir Hameed,
  • Arezoo Ariyaei

摘要

The climatic and hydrological variables play a significant role in water resources planning and management, due to their high spatial and temporal variability. While the classification of climatic regions based on various variables is challenging, effective zoning can improve our response to climate-induced hazards like floods and droughts. This study addresses these challenges by employing the C-means clustering algorithm to classify Iran based on monthly and annual precipitation, temperature, and meteorological drought, using observational data from 71 synoptic stations and satellite products, including ERA-5 and NASA-Power, spanning the years 1987 to 2017. The findings of this study indicate that the monthly performance of the ERA-5 satellite surpasses that of NASA-Power. Specifically, the ERA-5 satellite exhibited optimal performance in September and October, achieving R² values of 0.76 and 0.70, respectively, while NASA-Power recorded its highest performance in May and June, with R² values of 0.59. Conversely, the annual precipitation performance demonstrated superior results. The classification analysis revealed that the Central Plateau of Iran constitutes the largest region, consistently classified within the 1 to 2 class range across all scenarios, encompassing 50% of the country’s total area. The findings indicated that the northwest area, characterized by a cold and frigid climate, is consistently grouped in the same category across most terrestrial and satellite scenarios, encompassing approximately 13% of Iran’s territory within this climate. In the majority of scenarios, the northern part of Iran, which experiences the highest levels of rainfall, is divided into two distinct regions, both of which occupy the smallest land area, each being less than 1% of Iran’s total expanse. All scenarios categorize Iran into 5 to 9 classes, with the 5-class scenario corresponding to monthly precipitation and SPI 3 yielding 9 classes. Based on the homogeneity results, the classification derived from monthly and annual precipitation data is identified as the most effective. Furthermore, the results suggest that the classification based on NASA-Power satellite data aligns more closely with observational classifications. Effective climate zoning can contribute to environmental sustainability by optimizing resource management and minimizing the adverse impacts of climate change. The findings of this study can be beneficial for agriculture, selecting appropriate dam sites, and effective water resource management.