The Impact of Extreme Temperature and Precipitation on Dust Storm Trends in Southeastern Iran

Document Type : Research Article

Authors

1 PhD in Desert Management and Control, Combat to Desertification Department, Faculty of Desert Studies, Semnan University, Semnan, Iran

2 Professor, Combat to Desertification Department, Faculty of Desert Studies, Semnan University, Semnan, Iran

3 M.Sc. in Remote Sensing, Iranian Space Research Institute, Tehran, Iran

4 Assistant Professor, Iranian Space Research Institute, Tehran, Iran

5 Assistant Professor, Agricultural Education and Extension Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

Abstract

This study investigated the impact of extreme temperature and precipitation indices on the trend of dusty days in southeastern Iran. Extreme temperature and precipitation indices were selected based on local requirements and climatic characteristics of the study region, resulting in a total of eight precipitation indices (Rx1, Rx5, SDII, R10mm, CDD, CWD, and PRCPTOT) and six temperature indices (SU25, TXx, TXn, TX90p, WSDI, and DTR). These indices were derived using daily temperature and precipitation data from stations in the research area over 21 years, from 2000 to 2020, processed using the RClimDex software. A multilayer perceptron neural network was employed to assess the influence and importance of these temperature and precipitation indices on the trend of dusty days in south-eastern Iran. Additionally, the Mann-Kendall test and linear regression were used to analyze the trends in the investigated variables. The results revealed a significant decrease in the pattern of dusty days at the Rudan, Saravan, Iranshahr, Zabol, Bandar Abbas, Bam, and Khash stations. Among these, Zabol and Bandar Abbas recorded the highest number of dusty days over the study period, with annual averages of 171 and 93 days, respectively. The perceptron neural network model indicated that the extreme precipitation indices CDD, R10, and R20, along with the extreme temperature indices SU25, DTR, and TXn, exhibited the strongest correlations with the trend in dust storm variations. These findings provide valuable insights for developing more precise planning and policymaking strategies for dust storm management and adaptation to climate change-related impacts.

Keywords

Main Subjects

References

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History

  • Receive Date: 25 January 2025
  • Revise Date: 10 March 2025
  • Accept Date: 12 April 2025
  • First Publish Date: 12 April 2025
  • Publish Date: 21 March 2026

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