Using spatial statistics to identify drought-prone regions (A case study of Khuzestan Province, Iran)

Abstract

Iran is located in the Earth’s arid zone, and a drought crisis imperils the country as a result of declining water resources. Khuzestan Province, located in the south of Iran, is in critical condition due to water shortages; many of its groves have been destroyed. It also has many respiratory and pulmonary patients due to the constant presence of dust. The pandemic and this dust have caused acute problems for those diagnosed with COVID-19. Due to the importance of water deficit in this province, the present research calculated the Standardized Precipitation Index (SPI) and Standard Precipitation Evaporation Index (SPEI) in a thirty-year statistical period from 1984 to 2014; 12 stations were selected during the months when rainfall was more likely. This study utilized a geostatistical method to prepare zoning maps of SPI and SPEI. Then, various spatial statistics techniques in ArcGIS software were used to identify and locate the exact areas that were the sources of drought with the help of drought hot spots and strong drought clusters. Anselin Local Moran's maps indicated that the high-high precipitation clusters were located in the northeastern regions of Khuzestan. The hot and cold drought spots, which were identified by Getis-Ord G* spatial statistics based on both SPI and SPEI, showed that the hot spots were formed in the southern and southwestern regions; the cold spots were formed in the northwestern regions. Furthermore, the drought hot spots were identified with a 99% confidence level in places where the total ten-year precipitation was less than 270 millimeters.

Keywords

• spatial statistics
• geostatistics
• spatial correlation
• anselin local moran index
• hot spots and cold spots