ABSTRACT
BEST-FITTING AND RETURN PERIOD ANALYSIS AT KHULNA STATION DURING 1950-2022
Journal: Environment & Ecosystem Science (EES)
Author: Md. Bashirul Islam, Md. Nour Hossain, Md. Abul Hasan, Md. Mehedi Hassan Masum, Md. Ashraful Islam
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/ees.01.2024.53.59
Rainfall and temperature are crucial weather parameters in addressing climate change. Understanding the magnitude and severity of extreme events is essential for mitigating the adverse effects of climate change. This study conducted a frequency analysis of monthly maximum rainfall and temperature data for Khulna station spanning 73 years (1950–2022) to identify the best-fit distribution models capable of predicting extreme events. Among the eight probability distribution models (Normal, Lognormal, Generalized Extreme Value (GEV), Extreme Value, Logistic, t Location-Scale, Gamma, and Weibull distribution) tested, GEV proved the best fit for rainfall data, while Extreme Value was the optimal choice for temperature data, as confirmed by statistical tests (AIC, K-S and A-D). Model parameters were calculated using the log-likelihood method. Furthermore, the study estimated extreme values of maximum rainfall and temperature for return periods of 5, 10, 25, 50, 100, and 500 years. These findings can offer valuable insights for developing plans and strategies to mitigate the risks and damages associated with extreme weather events.
| Pages | 53-59 |
| Year | 2024 |
| Issue | 1 |
| Volume | 8 |
