The Effect of Climate Change in Rajshahi, Bangladesh: An Analysis of Seasonal Trends and Their Implications
Keywords:
Bangladesh, Climate change, Extreme weather, Rajshahi, Seasonal trendsAbstract
Climate change has intensified the frequency and severity of extreme weather events, particularly heat waves, which have significant consequences for public health, agriculture, and the economy. This study examines the impact of climate change on heat waves in Rajshahi, Bangladesh, focusing on past, present, and future trends. Using temperature data from the Bangladesh Meteorological Department (BMD) and Weather Spark, we analyzed historical trends from 1990 to 2024. Statistical analyses, including box and whisker plots, line graphs, and scatter plots, were employed to assess changes in temperature extremes, heat wave frequency, and intensity. The results show a clear upward trend in heat wave occurrences and duration, consistent with global climate change patterns. Projections under different climate scenarios suggest that Rajshahi will experience more intense and frequent heat waves in the future, posing significant risks to human health and agriculture. Results indicate that colder months have become cooler (lowest temperature about 7˚C) and hotter months hotter (highest temperature about 46˚C); however, the observed variations do not fully explain the extreme discomfort felt by residents during summers and winters. Significant seasonal variations in temperature and humidity are noted, particularly in January and March. Correlation analysis using R-square values reveals a range of 0.1 to 0.6 across different months, indicating that temperature and humidity trends are not strongly predictive. To better predict future variations, additional data, and advanced modeling techniques are required. Despite the study’s limitations, the identified trends offer valuable insights for future research and urban planning in Rajshahi. More relevant parameters and an extensive dataset are required to establish a strong correlation and accurate model for prediction.
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