نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسندگان English
Aim: Climate change, as a critical global issue, is primarily driven by human activities. This study aims to project changes in the mean maximum temperature across six meteorological stations in Sistan and Baluchestan province: Iranshahr, Chabahar, Zahedan, Zabol, Saravan, and Konarak. Projections were performed using CMIP6 models for the baseline period (1987–2014) and the middle future period (2051–2076).
Material & Method: To forecast temperature changes, CMIP6 models were applied under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios using data from six meteorological stations. The SDSM method was used for statistical downscaling, and model performance was assessed with MAD, MAPE, and RMSE indices. The Mann-Kendall test analyzed trends in average maximum temperature. IDW interpolation in GIS was used to map spatial temperature changes. Paired-sample t-tests evaluated differences between baseline and mid-future periods.
Finding: The CanESM5 model performed best in predicting temperature changes. The highest increase (31.2°C) occurred in July at Iranshahr under SSP5-8.5, while the lowest (0.63°C) was in Zahedan under SSP1-2.6. Annually, Iranshahr saw the highest rise (1.75°C) and Zahedan the lowest (1.34°C). Seasonally, summer at Iranshahr recorded the highest increase (2.24°C) under SSP2-4.5, and winter at Zahedan the lowest (0.82°C) under SSP1-2.6.
Conclusion: The study predicts an increase in the average maximum temperature at all stations, with the highest rise in summer at Iranshahr and the lowest in winter at Zahedan. Geographical and climatic factors significantly influence temperature patterns. Continuous monitoring and improved management strategies are crucial for mitigating climate change.
Innovation: This study utilizes advanced CMIP6 models to predict changes in the mean maximum temperature in Sistan and Baluchestan Province and provides spatial maps of these changes using the IDW interpolation method in a GIS environment. Additionally, it analyzes the relationship between temperature changes and local geographical and climatic conditions, offering valuable data for resource management planning and adaptation to climate change.
کلیدواژهها English
Climate change and global warming are among the most significant contemporary challenges, with widespread impacts across the globe. The Sistan and Baluchestan province of Iran is no exception, experiencing noticeable effects of these climatic changes. Assessing the future impacts of rising temperatures in this region requires utilizing advanced global climate models. The Coupled Model Intercomparison Project (CMIP) has made substantial progress in climate modeling through its various phases, including CMIP4, CMIP5, and CMIP6. The sixth phase of this project (CMIP6), launched in 2014, introduced new Shared Socioeconomic Pathways (SSPs), higher spatial resolutions, and more precise simulations, enabling more accurate climate predictions and better management of uncertainties. Numerous studies have employed CMIP6 models to investigate temperature and precipitation changes in different regions worldwide. These studies consistently indicate that annual and seasonal mean temperatures are expected to rise significantly across most regions during the 21st century. In this research, the outputs of CMIP6 models and various SSP scenarios have been utilized to examine changes in the mean maximum temperature in the Sistan and Baluchestan province. The primary aim of this study is to provide precise information for designing appropriate management strategies and mitigating the adverse effects of climate change in this region.
The study area, Sistan and Baluchestan Province covers 181,785 km² in southeastern Iran and is characterized by a desert climate with low annual precipitation (116.4 mm). It is the least densely populated province in Iran, with a population of 2,775,014.Daily data from six meteorological stations (Iranshahr, Chabahar, Zahedan, Zabol, Saravan, and Konarak) over the period of 1981-2014 were analyzed. Additionally, outputs from the CMIP6 model for the historical period (1979-2014) and future projections (2015-2100) were utilized. Three climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) were selected for the analysis. Three Global Climate Models (GCMs), CanESM5, MPI-ESM1-2-HR, and NorESM2-MM, were downscaled using the SDSM model.The models' performance was evaluated using error metrics (MAD, RMSE, MAPE), and the CanESM5 model was chosen for temperature simulation due to its higher accuracy. The trend of temperature changes was assessed using the Mann-Kendall test, and temperature maps were generated using IDW interpolation in GIS. Paired-sample t-tests were applied for mean comparisons, with a significance level set at p < 0.05.
The results indicate that the CanESM5 model outperformed other models in predicting temperature variations. The highest temperature increase was observed in July under the SSP5-8.5 scenario at Iranshahr station, while the lowest increase was recorded at Zahedan station under the SSP1-2.6 scenario. The seasonal trend analysis revealed that the most significant temperature rise will occur in summer, whereas the least increase is expected in winter. Statistical analyses demonstrated a significant difference between the mean maximum temperature in the baseline and mid-future periods. Furthermore, geographical and climatic factors such as elevation, vegetation cover, atmospheric humidity, and greenhouse gas effects play a crucial role in shaping the temperature variation patterns across the province. These findings underscore the necessity of precise planning and the development of management strategies to adapt to climate change and mitigate its adverse impacts.
This study investigates the climate changes in six meteorological stations in the Sistan and Baluchestan province, namely Iranshahr, Chabahar, Zahedan, Zabol, Saravan, and Konarak, during the base and mid-future periods. Data from three different General Circulation Models (GCMs), CanESM5, MPI-ESM1-2-HR, and NorESM2-MM, were used for the analysis. The results indicate that the CanESM5 model performed the best among the three. The projections show the highest increase in maximum temperature in July under the SSP5-8.5 scenario at the Iranshahr station, while the lowest increase is observed under the SSP1-2.6 scenario at the Zabol station. Additionally, the highest temperature increase in the summer season is projected to occur at the Iranshahr station under the SSP2-4.5 scenario, while the lowest increase is expected in the winter at the Zahedan station under the SSP1-2.6 scenario. Spatial analysis results indicate that factors such as latitude, elevation above sea level, atmospheric humidity, vegetation cover, and greenhouse gas effects contribute to temperature variations across stations in the province. This research highlights the significant ongoing climate changes in the region, which are expected to continue.
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