Forecasting future climate boundary maps (2021–2060) using exponential smoothing method and GIS


Science of the Total Environment, vol.848, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 848
  • Publication Date: 2022
  • Doi Number: 10.1016/j.scitotenv.2022.157633
  • Journal Name: Science of the Total Environment
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, MEDLINE, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Climate boundaries, Erinc climate classification method, Forecast map, GIS, Triple exponential smoothing method
  • Ankara Haci Bayram Veli University Affiliated: Yes


Future-oriented forecasts have an important place in making forward-looking decisions and planning. At the beginning of these studies is the monitoring and detection of climate change. The climate is very variable. Therefore, by making predictions about the climate, preliminary information about how and to what extent the climate will change can be obtained, and accordingly, necessary precautions can be taken quickly. This study aims to produce predictive climate boundary maps using Geographic Information Systems (GIS), in which climate classification methods and time series methods are evaluated to monitor and determine the changes caused by the climate in 13 selected provinces in Turkey. The triple exponential smoothing method and the Erinc climate classification method were discussed. The data were obtained from the General Directorate of Meteorology (GDM) between 1930 and 2020, and each year's precipitation efficiency index (Im) of the Erinc climate classification method was calculated. It is divided into two classes for forecasting and testing current indices: test Im indices (1930–2014) and forecast test Im indices (2015–2020). MAD, MSE, and MAPE criteria were calculated to determine whether the Im estimates were meaningful. However, the accuracy of the estimates was ensured by considering the MAPE criteria for this study. After this stage, the analyses were performed again with test Im indices (1930–2020) and forecast Im indices (2021–2060), and Im indices predictions for the future were made. Finally, the obtained forecast indices were subjected to GIS interpolation analyses (Kriging and IDW), and future climate boundary maps were produced. Thanks to the outputs obtained from the study, how the climate classes of any region will be in the future and to what extent they will change will be provided by evaluating the climate classification and time series methods together. It will contribute to different studies in this field with its innovative analysis approach.