Impact of Climate Change on Precipitation Extremes Indices over Iran

Document Type : Research

Authors

1 Ph.D. Graduated in Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran

2 Assistant Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Malayer University, Malayer, Iran

3 Professor, Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources, University of Hormozgan, Bandar Abbas, Iran

4 M.Sc. Graduated in Watershed Management, Department of Nature Engineering, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

10.22092/wmrj.2024.364521.1565

Abstract

Introduction and Goal
Climate change leads to changes in extreme climate indices, such as an increased intensity and frequency of extreme weather events. Heavy precipitation during these events can cause widespread damage to the economy, the environment and human life. According to the World Economic Forum's (WEF) Global Risks Report 2019, extreme climate events (such as heavy precipitation) and climate inadaptability are the greatest risks in terms of their impact on the environment and people. The purpose of this study was to examine the impact of climate change on heavy precipitations in the Iran.
Materials and Methods
Precipitation data from 47 stations was utilized in this study. First, the performance of the model was evaluated. LARS-WG software was employed to predict precipitation data with various scenarios for the future period. To evaluate the simulation of extreme precipitation using the aforementioned model, in addition to graphically comparing the observed and generated data of each station in the validation process, the RB, MD, and WS indices were calculated between the observed and simulated extreme index data. Finally, the Mann-Kendall test was utilized to examine the pattern of heavy precipitation events.
Results and Discussion
The simulation results for precipitation in the period 2030-2050 show that the synoptic stations will experience uneven annual fluctuations. The largest increase in annual precipitation was 59.81% (32.32 mm) at the Bam station and SSP5-8.5 scenario, and the largest decrease in annual precipitation was 11.63% (17.23 mm) at the Kish station and SSP5-8.5 scenario. According to the spatial distribution of PRCPTOT, the Yasouj station experienced the highest increase in annual precipitation under the SSP1-2.6 scenario. At this station, the annual precipitation for the future period was estimated to be 933.49 and 889.3 mm with the SSP1-2.6 and SSP5-8.5 scenarios respectively. Also, the magnitude of this indicator during the base period at this station was predicted to be 832.19 mm.
Furthermore, it was predicted that this indicator will be 832.19 mm during the base period at this station. The results of the study of changes in precipitation extreme indices in the period 2030-2050 compared to 1985-2022 showed that the greatest changes were in the northern and western regions of the Iran, and the severity of these changes was especially greater with the SSP5-8.5 scenario. The largest decrease in the Rx1day index was related to the Mashhad station with the SSP5-5.5 scenario, which was 37%. The highest increasing trend was related to the Zanjan station in the RCP8.5 scenarios.
Conclusion and Suggestions
Numerous studies around the world have shown that climate change leads to changes in extreme precipitation. A decrease in precipitation intensity and a change in precipitation patterns are among these changes. According to the results of this study, the trend of extreme indices was not uniform, and it was increasing in some synoptic stations and decreasing in others. In general, precipitation intensity is expected to increase in the future and with climate scenarios in most of the studied stations. The impact of extreme precipitation events on future planning and policymaking in various sectors should be carefully examined. Therefore, it is suggested that similar studies be conducted in the country's main watersheds on a smaller scale and in a larger number of stations with other trend detection tests, and the results be compared with the Mann-Kendall statistical test.

Keywords

Main Subjects

References

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History

  • Receive Date: 07 January 2024
  • Revise Date: 14 February 2024
  • Accept Date: 18 March 2024

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