Impact of Climate Change on Rainfall Pattern Variability in Ilam Province Using CMIP6 Models and Rainfall Frequency Analysis

Document Type : Research Paper

Authors

1 Water Engineering Department, Faculty of Agricultural Technology, University College of Agriculture & Natural Resources, University of Tehran

2 Department of Water Engineering, Faculty of Agriculture Technology, College of Agriculture and Natural Resources, University of Tehran, Iran

Abstract

The global warming trend has raised concerns about the state of water resources. In this study, to examine the impact of climate change on the precipitation pattern of Ilam Province, the outputs of 12 CMIP6 models were utilized. Considering the SSP1-2.6 and SSP5-8.5 climate change scenarios, precipitation variations in Ilam Province were analyzed up to the year 2050. After clustering the rain gauge stations (Cluster 1: South and East of the province, Cluster 2: North and West of the province) and evaluating the performance of CMIP6 models, the IPSL-CM6A-LR and ACCESS-CM2 models were selected as the best models for Cluster 1, while the IITM-ESM and BCC-CSM2-MR models were chosen for Cluster 2. The model outputs indicate that in the future period (2018-2027), for Cluster 1, under the SSP1-2.6 scenario, the average annual precipitation will decrease by 4.1% to 303.52 mm per year. Under the SSP5-8.5 scenario, this reduction will be 4.7%, bringing annual precipitation to 301.56 mm per year. For Cluster 2, under the SSP1-2.6 and SSP5-8.5 scenarios, the average annual precipitation will increase by 3% and 2.8%, respectively, rising from 431.10 mm per year to 444.04 mm per year (SSP1-2.6) and 443.31 mm per year (SSP5-8.5). By selecting the best probabilistic distribution for each rain gauge station, it was found that under both climate change scenarios and different return periods, the maximum 24-hour precipitation in most cases will decrease in the future period. Therefore, this highlights the importance of developing sustainable water supply strategies for the province.

Keywords

Main Subjects

EXTENDED ABSTRACT

 

Introduction

Climate change has brought destructive effects on agriculture, biodiversity, and water resources. General Circulation Models (GCMs) are among the most commonly used tools for studying climate change on global and regional scales, producing climate scenarios for the present and future. One of the parameters affected by climate change is precipitation, the variation of which can set the stage for alterations in water resource management scenarios in any given region. Considering the background of other research, analyzing climate change and its impact on climate parameters like precipitation can provide a suitable perspective for long-term watershed management. Therefore, in this study, by evaluating 12 CMIP6 climate models to predict precipitation changes up to the year 1427, the most accurate model for prediction was selected, and based on it, precipitation changes were analyzed. The results of this research can be used with a high level of accuracy to develop management strategies for better management of Ilam province for adapting to the consequences of climate change in the years ahead.

Method

Ilam province is located in western Iran, covering approximately 19,350 km2. The province shares borders with Kermanshah to the north, Lorestan to the east, Khuzestan to the southeast, and Iraq to the west. The study examines the impact of climate change on rainfall patterns in Ilam province, analyzing data from 11 rain gauge stations scattered throughout the region from October 1991 to September 2018. Clustering analysis is utilized to categorize the rain gauge stations and study the changes in rainfall patterns due to climate change. Historical precipitation data from 1995 to 2014 is used to assess future rainfall variations and determine the climate parameters' effects on Ilam province, with simulations up to 2050 using twelve CMIP6 models. The research employs statistical methods for downscaling and bias correction of CMIP6 model data, including Quantile Mapping for scaling adjustments.

Results

For Cluster 1 and under the SSP1-2.6 scenario, it is observed that the average values of all indices except CWD have decreased compared to the baseline period, indicating a decrease in dry days and an increase in wet days. Overall, the number of days with heavy and extremely heavy rainfall has decreased, and for SSP5-8.5, the average indices of CDD, CWD, PRCPTOT, R10mm, and R20mm have increased while the average of the remaining indices has decreased. This shows that under the SSP5-8.5 scenario, the intensity of daily precipitation has decreased, but the number of wet days and total precipitation have increased. Under the SSP1-2.6 scenario, the maximum daily precipitation at Station 2 for return periods of 100 and 1000 years is 75.1% and 47.12% respectively, while at Station 6 and for return periods of 2 and 5 years, it is 9.7% and 22.2% respectively, and at Station 10 for return periods of 2, 5, and 10 years, it increases by 42.22%, 78.9%, and 58.0% respectively, with maximum daily precipitation decreasing in other cases. Under the SSP5-8.5 scenario, the maximum daily precipitation at Station 1 for return periods of 2 and 5 years is 101.2% and 98.0% respectively, and at Station 2 for return periods of 25 to 1000 years, it increases by 4.6%, 59.11%, 79.16%, and 79.34% respectively, while at Station 7 for a 1000-year return period, the maximum daily precipitation has increased by 68.3%. Knowing the increase in maximum rainfall values can lead to the development of water resource management scenarios with higher accuracy.

Conclusions

The results showed the average annual rainfall in clusters one and two during the observational period is 5.316 and 1.431 millimeters per year, respectively. For cluster one, according to the SSP1-2.6 scenario, the average annual rainfall is projected to decrease by 1.4% by 2049, reaching 52.303 millimeters per year, while under the SSP5-8.5 scenario, this decrease is 7.4%, with an annual rainfall of 56.301 millimeters during this period. In cluster one, the average annual rainfall in the autumn and spring seasons in the future will decrease, while the winter season's rainfall will increase. For cluster two, the average annual rainfall in the winter and autumn seasons will increase under the SSP5-8.5 scenario, while there will be a decrease in rainfall during the autumn season under the SSP1-2.6 scenario and in the spring season. Climate change in Ilam Province leads to a reduction in precipitation, endangering the water resources in the region. Therefore, it is recommended to develop strategies and adaptation scenarios to cope with the climate phenomenon by focusing on reducing precipitation, to achieve sustainable water resource management in years facing water scarcity.

Author Contributions

“Conceptualization, Saman Javadi and Farhad Behzadi; methodology, Ali Mohammadi; software, Farhad Behzadi; validation, Farhad Behzadi., Ali Mohammadi; formal analysis, Farhad Behzadi; investigation, Saman Javadi and Ali Mohammadi; resources, Farhad Behzadi; writing—original draft preparation, Ali Mohammadi; writing—review and editing, Saman Javadi and Ali Mohammadi.”

“All authors have read and agreed to the published version of the manuscript.”

Data Availability Statement

Data available on request from the authors.

Ethical considerations

The study was approved by the Ethics Committee of the University of Tehran (Ethical code: IR.UT.RES.2024.500). The authors avoided data fabrication, falsification, plagiarism, and misconduct.

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Iranian Journal of Soil and Water Research
Volume 55, Issue 12
March 2025
Pages 2421-2441

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