Extensive rainfall data analysis: event separation from continuous record, fitting of theoretical distributions, and event-based trend detection†
Abstract
The impact of climate change on the environment, particularly water resources, can never be over-emphasized. Therefore, it is imperative to continuously study and understand climate data, especially on a regional scale. This paper used the sites in the four nations' capitals in the United Kingdom as the study areas with extensive rainfall data collected from a total of six weather stations. The article is the first study conducted on extensive hourly rainfall data observed at selected weather stations in the four nations of the UK for exponentiality testing of their rainfall characteristics before carrying out a comprehensive comparative analysis of their distribution parameters and conducting event-based trend analysis to investigate the potential impact of climate change on the regional rainfall characteristics. Several earlier studies have considered separating continuous rainfall data into individual rainfall events as the most challenging and time-consuming aspect of rainfall data analysis. Therefore, first, this paper provided a step-by-step guide on rainfall event separation, especially for data with missing values, before conducting frequency analysis to test the exponentiality of rainfall event characteristics using large samples. The results from the study indicated that the rainfall event characteristics observed at the weather stations in the four nations of the UK were both exponentially and gammally distributed. The comparative analysis of the distribution parameters demonstrated that the observed rainfall data have heterogeneous spatial distributions. The event-based trend analysis showed an upward trend in the number of extreme rainfall events and the number of rainfall events per year in the observed data. However, there was no correlation between temperature and the rainfall characteristics, although, in a global context, a correlation is suggested.