Akademik Veri Yönetim Sistemi
Natural Hazards, 2025 (SCI-Expanded)
With the increasing impacts of climate change, studies based on trend analysis have recently increased and are considered essential tools in different sectors, such as the agricultural sector. Compared to classical trend methods, the innovative trend analysis (ITA) methodology provides a robust visualization and interpretability. Despite its advantages, there is a need to introduce a novel framework of ITA (ITA-NF) to enhance its interpretability by incorporating the scatter plots, statistical classification approach based on the standardization concept and corresponding frequencies and expanding its application to extreme precipitation indices. This allows for an objective examination and interpretation of the effects of climate change. So, the main objective of this research is to propose the ITA-NF by Improving the original Şen's ITA methodology. Daily precipitation data from two stations with different periods and climates were used as applications in this research: the Durham station in the UK from 1868 to 2021 and the Burbank station in California, USA, from 1940 to 2023. The results showed that dividing the data into classifications with their corresponding frequencies improves our understanding of climate change and objective interpretation of how the sub-different trends and trend conditions are distributed within the dataset. For the annual maximum consecutive 5-day precipitation (RX5d) at Burbank station, the first (second) half of the ML–SL total frequency was 40.47% (35.72%), and the NL-NH total frequency was 33.33% (42.86%). ITA-NF can be utilized in climate adaptation and mitigation strategies, and it can understand all trend in the data throughout time.