Scholarlink

<p>Understanding the impacts of climate change on precipitation patterns in Iran is critical, given its predominantly arid and semi-arid geography. This study assesses the variability of seasonal and annual precipitation across the Zagros Mountains, which span approximately 1,500&#xa0;km across Iran. To achieve this, historical precipitation data from 269 reliable meteorological and rain gauge stations, covering the period 1950 to 2015, were analysed. Trends in precipitation series were identified using linear regression, with their significance assessed through t-test. To validate these results, additional non-parametric and correlation-based methods were applied, including the Mann–Kendall test (MK), Spearman’s rank correlation, and Pearson correlation coefficient. The Sequential Mann–Kendall test (SQ-MK) was also utilized to detect change points within the time series. To project future precipitation trends from 1981 to 2100, three models from the Coupled Model Intercomparison Project Phase 6 (CMIP6)—GFDL-ESM4.1, MPI-ESM1-2-LR, and MRI-ESM2-0—were employed under three Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. This analysis provides a more detailed assessment of precipitation variability compared to prior research works across the Zagros Mountains. Most stations revealed statistically insignificant trends in historical precipitation at the 95% confidence level. Among the 269 stations with significant trends, 35 stations (13%) showed positive trends in summer, although the magnitude of the increase was relatively small. Whereas winter, spring, and annual series generally showed negative trends at 33 (12.3%), 27 (10%), and 23 (8.6%) sites, respectively. The most significant shifts in precipitation patterns were identified over the past two decades. Across most models and scenarios, projections indicate a general decline in precipitation, with winter experiencing the most significant reduction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Historical trend analysis and projection of precipitation by CMIP6 models across the Zagros Mountains of Iran

  • 期刊论文

  • 发表日期：

    2026年4月11日

查看全文
  • Matin Alinejad,
  • Amit G. Dhorde,
  • Ehsan Taghizadeh,
  • Tanmay S. Jadhav,
  • Sadegh Zeyaeyan

摘要

Understanding the impacts of climate change on precipitation patterns in Iran is critical, given its predominantly arid and semi-arid geography. This study assesses the variability of seasonal and annual precipitation across the Zagros Mountains, which span approximately 1,500 km across Iran. To achieve this, historical precipitation data from 269 reliable meteorological and rain gauge stations, covering the period 1950 to 2015, were analysed. Trends in precipitation series were identified using linear regression, with their significance assessed through t-test. To validate these results, additional non-parametric and correlation-based methods were applied, including the Mann–Kendall test (MK), Spearman’s rank correlation, and Pearson correlation coefficient. The Sequential Mann–Kendall test (SQ-MK) was also utilized to detect change points within the time series. To project future precipitation trends from 1981 to 2100, three models from the Coupled Model Intercomparison Project Phase 6 (CMIP6)—GFDL-ESM4.1, MPI-ESM1-2-LR, and MRI-ESM2-0—were employed under three Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. This analysis provides a more detailed assessment of precipitation variability compared to prior research works across the Zagros Mountains. Most stations revealed statistically insignificant trends in historical precipitation at the 95% confidence level. Among the 269 stations with significant trends, 35 stations (13%) showed positive trends in summer, although the magnitude of the increase was relatively small. Whereas winter, spring, and annual series generally showed negative trends at 33 (12.3%), 27 (10%), and 23 (8.6%) sites, respectively. The most significant shifts in precipitation patterns were identified over the past two decades. Across most models and scenarios, projections indicate a general decline in precipitation, with winter experiencing the most significant reduction.