State-of-the-art visualization for innovative trend analysis

Abstract

Trend analyses play a fundamental role in understanding the impacts of climate change, helping to uncover long-term patterns of change and predict their impacts on the environment and societies. This research introduces a new framework combining different lengths (DL) Innovative Trend Analysis (ITA) methodology, with the frequency ITA approach. The proposed method is designed to identify monotonic, non-monotonic, homogeneous, and heterogeneous trends in streamflow, precipitation, and temperature data. A key advantage of the concept is its unique graphical potential, which enhances the interpretation of complex trend behaviors and patterns. In this methodology, the data are classified by considering the probability values corresponding to certain well-known basic standard deviation values, resulting in Frequency DL-ITA (FDL-ITA). It aims to provide objective integrity between the interpretations of different application regions and data sets. The proposed approach and graphs were used to determine the trends of precipitation, temperature, and streamflow data in different climates and regions: Oxford station, UK (1784-2023), Florya station, T & uuml;rkiye (1937-2024), Orsova, Danube River (1843-2022), and Novosaratovka, Russia (1903-2022). The results showed a general decrease in streamflow at Orsova station for all seasons except winter, with more frequent and severe reductions in the last 20 years. Novosaratovka displayed no consistent trend in all seasons. At Oxford station, a monotonic increasing trend was observed in all classifications for annual precipitation. In contrast, the trend of seasonal precipitation in the last 22 years was decreasing for the Florya station. For temperature trends, both Oxford and Florya stations recorded significant warming, with a substantial increase in the frequency of extreme high temperatures over the last 22 years. At Oxford, the frequency of extreme high temperatures increased from 5% to 50% compared to the previous 180 years, highlighting the accelerating impact of climate change. The findings indicated that the choice of time periods substantially influences trend identification and analysis, highlighting the need for adaptable methodologies in climate research. In summary, FDL-ITA with improved visualization can enhance the interpretation of trend analyses by capturing both short-term and long-term patterns. This can help decision-makers and policymakers in climate change adaptation strategies and water resources management plans.