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<p>Climate change is significantly altering rainfall patterns, especially in regions with diverse landscapes. Focusing on the climate-sensitive Upper Meghna River Basin of Bangladesh, this study analyzes 38 years (1987–2024) of monthly, annual, and seasonal rainfall trends across seven meteorological stations. Rainfall trends were assessed at both the 5% and 10% significance levels. While most monthly trends were only significant at the 5% level, notable findings include an increasing trend in August rainfall at Chandpur (+ 6.29&#xa0;mm/year) and decreasing trends in September at Tangail (–3.75&#xa0;mm/year) and in February at Mymensingh (–0.65&#xa0;mm/year). Additionally, Tangail exhibited a statistically significant decrease in both annual (–11.33&#xa0;mm/year) and monsoonal (–9.81&#xa0;mm/year) rainfall at the 10% level, while other stations showed irregular patterns with no significant trends. The study further explored the influence of major climatic drivers, including the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), using Pearson correlation analysis with 1–6 month lagged effects. The results revealed weak associations with ENSO (<i>r</i> = 0.003) and IOD (<i>r</i> = − 0.014), with slightly increased values at a 6-month lag for ENSO (<i>r</i> = 0.039) and a 5-month lag for IOD (<i>r</i> = 0.068), suggesting minimal lagged influence on regional rainfall variability. In predictive modeling, CatBoost outperformed XGBoost, demonstrating strong capacity in capturing rainfall dynamics. These findings provide critical and actionable insights for water resource planning and climate adaptation in the study region.</p>

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Modeling the rainfall and large–scale climate influence in the Upper Meghna River Basin of Bangladesh

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Md. Musa Nadim,
  • Md. Anowarul Islam,
  • Shah Md Shajib Hossain,
  • Shahrin Kabir Mowmi,
  • Md. Aminul Haque Laskor,
  • Toru Terao

摘要

Climate change is significantly altering rainfall patterns, especially in regions with diverse landscapes. Focusing on the climate-sensitive Upper Meghna River Basin of Bangladesh, this study analyzes 38 years (1987–2024) of monthly, annual, and seasonal rainfall trends across seven meteorological stations. Rainfall trends were assessed at both the 5% and 10% significance levels. While most monthly trends were only significant at the 5% level, notable findings include an increasing trend in August rainfall at Chandpur (+ 6.29 mm/year) and decreasing trends in September at Tangail (–3.75 mm/year) and in February at Mymensingh (–0.65 mm/year). Additionally, Tangail exhibited a statistically significant decrease in both annual (–11.33 mm/year) and monsoonal (–9.81 mm/year) rainfall at the 10% level, while other stations showed irregular patterns with no significant trends. The study further explored the influence of major climatic drivers, including the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), using Pearson correlation analysis with 1–6 month lagged effects. The results revealed weak associations with ENSO (r = 0.003) and IOD (r = − 0.014), with slightly increased values at a 6-month lag for ENSO (r = 0.039) and a 5-month lag for IOD (r = 0.068), suggesting minimal lagged influence on regional rainfall variability. In predictive modeling, CatBoost outperformed XGBoost, demonstrating strong capacity in capturing rainfall dynamics. These findings provide critical and actionable insights for water resource planning and climate adaptation in the study region.