Atıf İçin Kopyala
Alashan S., Abu Arra A., Şişman E.
PURE AND APPLIED GEOPHYSICS, cilt.1, sa.1, ss.1-2, 2024 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
1
Sayı:
1
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Basım Tarihi:
2024
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Doi Numarası:
10.1007/s00024-024-03525-w
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Dergi Adı:
PURE AND APPLIED GEOPHYSICS
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Geobase, INSPEC
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Sayfa Sayıları:
ss.1-2
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Yıldız Teknik Üniversitesi Adresli:
Evet
Özet
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.