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Using Generative Art to Convey Past and Future Climate Transitions

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

Tugba Memisoglu Baykal¹, H Ebru Colak², Cebrail Kılınc²

¹Artvin Coruh University, Department of Geomatics Engineering, Artvin, Turkey.

The Science of the Total Environment

|July 28, 2022

Summary

This study forecasts future climate changes in Turkey using climate classification and time series analysis. Predictive climate maps were generated to aid in planning and decision-making for climate adaptation.

Keywords:

Climate boundaries Erinc climate classification method Forecast map GIS Triple exponential smoothing method

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Area of Science:

Environmental Science
Climatology
Geographic Information Systems (GIS)

Background:

Climate change necessitates accurate forecasting for informed decision-making and planning.
Monitoring climate variability is crucial for understanding and mitigating climate change impacts.
Predictive modeling provides essential information for timely adaptation strategies.

Purpose of the Study:

To develop predictive climate boundary maps for 13 Turkish provinces using GIS.
To evaluate climate classification and time series methods for monitoring climate change.
To forecast future climate conditions and their extent of change.

Main Methods:

Utilized the Erinc climate classification method and triple exponential smoothing for time series analysis.
Calculated the precipitation efficiency index (I_m) using data from the General Directorate of Meteorology (1930-2020).
Employed GIS interpolation techniques (Kriging and IDW) on forecast indices to create climate boundary maps.

Main Results:

Forecasted future climate boundary maps were produced for the selected provinces.
The accuracy of I_m index predictions was validated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) criteria.
The study successfully integrated climate classification and time series analysis for future climate prediction.

Conclusions:

The developed methodology provides insights into future climate class changes and their magnitude.
This approach offers a valuable tool for regional climate change adaptation and planning.
The study contributes an innovative analysis framework to climate change research.