Standardized Innovative Polygon Trend Analysis for Climate Change Assessment (S-IPTA)

Alashan S., Abu Arra A., Şişman E.

PURE AND APPLIED GEOPHYSICS, vol.1, no.1, pp.1-2, 2024 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 1 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.1007/s00024-024-03525-w
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Geobase, INSPEC
  • Page Numbers: pp.1-2
  • Yıldız Technical University Affiliated: Yes


AbstractResearch and applications on trend analysis have recently been on the agenda and are top priorities in many disciplines due to the effects of climate change. After a thorough evaluation of the literature, it is noted that different hydro-meteorological variables, such as precipitation, temperature, etc., are studied and analyzed individually. This research proposes a new innovative polygon trend analysis application (S-IPTA) using the standardization concept to fill this gap in classical trend applications and comprehensively compare the trends of different variables to temporal and spatial patterns. Firstly, using statistical standardization, S-IPTA adjusts the original data sets and makes them dimensionless. Then, the innovative trend analyses are conducted and interpreted on one single graph (S-IPTA). The S-IPTA methodology is applied to monthly precipitation and temperature time series of Konya Basin in Türkiye at ten meteorological stations between 1959 and 2022. For precipitation, the S-IPTA did not exhibit a consistent polygon across all stations within the study area, while the temperature polygon was more regular, indicating that the temperature mean was generally stable with a positive trend. Also, S-IPTA shows the difference between the average value for each month and the newly proposed long-term average value (0). S-IPTA also provides a basis for a better interpretation of climate change and its effects by providing a common denominator for various trend characteristics, such as trend magnitudes and trend transitions in different hydro-meteorological time series.