International Research Journal of Education and Technology

The study aims to assess the precipitation pattern and different methods for identifying patterns. Non-parametric methods, such as the Mann-Kendall test, were employed for pattern analysis, while Sen's slope was used to determine the magnitude of the trend. Various studies have demonstrated the variability of precipitation worldwide and have also examined the autocorrelation effect in hydrological data. It was observed that precipitation patterns vary across different regions and are influenced by factors such as geography. The Sen's Slope Estimator, along with nonparametric and the Mann-Kendall Test for Monotonic Trend Analysis, was utilized to quantify the strength of trends in monthly, seasonal, and yearly precipitation data. One advantage of this approach is that it does not require the data to be normally distributed or conform to any specific criterion. However, it should be noted that the sample size may be limited due to missing data, which can affect statistical significance. Additionally, the uneven spacing of measurement time points does not impact the analysis. The Mann-Kendall approach has a limitation in that it tends to yield more negative findings for shorter datasets. Therefore, it is recommended to remove known periodic effects from the data before conducting the test. The chi-square test, which compares actual outcomes to predictions, can be employed to examine the relationship between two variables and identify discrepancies between observed and expected values. However, this test requires numerical data, a minimum of 20 observations, and should not be used to compare categories with only two options.

Key Words:RAINFALL TREND, RAINFALL PATTERN, FORECAST, AND TEMPERATURE.