Mapping the spatiotemporal diversity of precipitation in Iran using multiple statistical methods

Despite being located in a semi-arid and arid part of the world, Iran enjoys a very diverse climate. Our objective is to regionalize the country into homogeneous precipitation regions and determine the monthly and annual precipitation water volume and depth in each region, required in hydro-climatological studies and applications, from simple water budget calculation to infrastructure design. We investigate the spatiotemporal diversity of precipitation over the country by analyzing the 33-year-long monthly precipitation time series (1983-2016) at 461 rain-gauge stations. We employed cluster analysis (CA) both hierarchical and non-hierarchical clustering approaches and principal component analysis (PCA) to determine the homogeneous precipitation zones at three macro-, meso-, and micro-scales (resolutions). First, the country is divided into six macro-precipitation regions (MPRs) using CA each showing a mean annual hyetograph of unique pattern and depth. The Siberian cold continental air mass enters the country from the north, the Sudan air mass from the south and southwest, the Mediterranean air mass from the west, the North Atlantic and the Black Sea cyclones from the northwest, and the Maritime air mass from the southeast create these six precipitation regions. Then, the six regions were divided into ten zones of meso-resolution through hierarchical clustering (HC) and k-means clustering. The occasional collision of the air masses causes the division of the six macro-regions into ten zones at meso-resolution. Finally, we subjected the precipitation time series of ten meso-zones to PCA, HC, and k-means clustering and established an optimal number of 24 micro-zones for the first time that reflects a comprehensive precipitation map over the country. The annual hyetograph of each zone shows a unique pattern and distribution with a varying magnitude of monthly precipitation compared to others as the result of varied physio-geographical characteristics of the country prevailing in each micro-zone. The result shows that hierarchical clustering (Ward's method-Pearson correlation) and PCA have the same classification performance and strength in meso- and micro-climatological zoning. The long-term (i.e., 33 years) mean annual hyetograph in each region and zone is also calculated, and the monthly and annual-precipitation water volume and depth in the country are estimated. The findings provide the researchers, practitioners, and decision-makers with an accurate baseline reference for future research and water resource management and will advance the understanding of precipitation dynamics in different regions of the country.

Automated summary

This article investigates the spatiotemporal diversity of precipitation in Iran and determines homogeneous precipitation regions at different scales using cluster analysis and principal component analysis. The findings provide accurate baseline reference for water resource management and advance the understanding of precipitation dynamics in different regions of the country.