ABSTRACT
A TREND ANALYSIS OF RAINFALL IN KHULNA DISTRICT OF BANGLADESH
Journal: Big Data In Water Resources Engineering (BDWRE)
Author: Md. Sarwar Jahan, Afifa Tamim, Sanjida Akter Nishita and S.M. Abdullah Al Mamun
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
DOI: 10.26480/bdwre.02.2024.30.37
Bangladesh, despite having a subtropical monsoon climate characterized by a waterless winter and warm summer, is one of the peak susceptible countries to climate change. Recently, climate change has garnered significant attention from academics, researchers, and policymakers globally. This study examines the trends in annual as well as monthly rainfall in the Khulna district of Bangladesh over a 20-year period (2003-2022). The aim is to provide current insights into weather patterns, particularly rainfall, in the Khulna district. Secondary data on rainfall were obtained from the Regional Inspection Center (RIC), Bangladesh Meteorological Department in Gallamari, Khulna. Descriptive statistics i.e., mean, standard deviation, and coefficient of variation were estimated to describe the annual and monthly distribution of rainfall. Trend analyses were conducted using bivariate analysis, and simple regression was engaged to assess the relationship between years with rainfall data. The results revealed that monthly rainfall did not follow a consistent pattern, with both increasing and declining trends were detected over the years. When annual rainfall was plotted against years, a negative relationship was identified (y= -12.877x + 27742, R2 = 0.0489). Similarly, mean monthly rainfall showed a declining trend over time (y= -1.0731x + 2311.9, R2 = 0.0489). However, these relationships were not statistically significant. The study underscores the need to implement various adaptation strategies to ensure sustainable agricultural production in the Khulna region. It also suggests the necessity of enhanced monitoring methods due to the instability of temperature and rainfall patterns.