Trend analysis of precipitation using innovative approaches in northwestern Turkey

Abstract

Precipitation is highly sensitive to climate change, and monitoring its trends over time is essential for understanding the effects of global warming on the Earth's water cycle. This study investigates the effects of climate change on monthly precipitation in the Susurluk Basin located in northwestern Turkey, using innovative trend analysis methods. Innovative Polygon Trend Analysis (IPTA) using star graphics and Improved Visualization for Innovative Trend Analysis (IV-ITA) techniques were used to analyze precipitation data from nine stations in the basin over 50 years (1970–2019), and the findings were compared to classical Mann-Kendall (MK) results. According to the trend analysis results, the MK method detected significant trends in only two months in the decreasing direction. In these decreasing trends observed at Simav and Keles stations in November, z values were calculated as −2.38 and −2.05, respectively. IPTA method detected increasing trends in 54 months and decreasing trends in 40 months; this means there is a change of more than 5% between two half series. The IV-ITA method indicated an increase of more than 40% in the low values for January, March, August, and September. In addition, there was an increasing trend of more than 40% in the high values observed in September at all stations. It was observed that mean precipitation heights increased from September to December and then decreased throughout the basin. It is predicted that decreases in precipitation during dry seasons may put intense pressure on agricultural water use and affect water quality. In addition, the increase in precipitation in spring months may increase flood events. It was determined that graphical innovative trend analysis methods are more effective in trend identification than MK, and these methods' results have much higher visual inspection and linguistic interpretation possibilities. Innovative methods allow for more flexible and in-depth analysis using different statistical parameters. © 2023 Elsevier Ltd