A multi-method spatial interpolation framework for climate boundary mapping in the Aegean region of Türkiye
- 1Izmir Vocational School, Department of Architecture and Urban Planning, Dokuz Eylul University
Abstract
Understanding spatial variations in climate is essential for evaluating water balance, land use, and regional sustainability. This study proposes a GIS-based multi-method framework that integrates classical climate classification systems—Thornthwaite, de Martonne, Erinç, Köppen, Köppen–Geiger, and Trewartha—and employs a comparative geostatistical interpolation approach to delineate climate boundaries in an Aegean coastal region of Türkiye. Long-term climate-normal data (1991–2021) extracted from Climate-Data.org for four representative locations were used for climate classification analyses, whereas data from all nine geographically distinct locations were incorporated into the geostatistical interpolation to generate spatially continuous climate maps. The Kruskal–Wallis test was applied to evaluate differences in potential evapotranspiration (ETp) among stations, showing no statistically significant variation (p = 0.797). Among the six evaluated interpolation methods, Radial Basis Function (RBF) achieved the lowest prediction errors (RMSE = 4.342; R² = 0.758), while Empirical Bayesian Kriging (EBK) showed comparable performance (RMSE = 4.590; MAE = 3.271; R² = 0.729). Although RBF yielded slightly better cross-validation metrics, EBK was selected for spatial climate mapping due to its ability to explicitly model spatial autocorrelation and quantify prediction uncertainty, which is particularly advantageous under sparse and uneven station distribution. Thornthwaite precipitation effectiveness index (Im) values ranged from − 9.81 to 9.49, indicating predominantly dry to subhumid climatic conditions across the study area. The proposed GIS-based framework demonstrates that integrating multiple climate classification systems with comparative interpolation analysis improves the delineation of regional climate boundaries and provides a transferable methodology for climate mapping in data-limited coastal environments.
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BibTeX
@article{arca2026multimethod,
title = {{A multi-method spatial interpolation framework for climate boundary mapping in the Aegean region of Türkiye}},
author = {Deniz Arca},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-72166-x},
url = {https://doi.org/10.1038/s41598-026-72166-x},
note = {CC BY},
}
RIS
TY - JOUR
TI - A multi-method spatial interpolation framework for climate boundary mapping in the Aegean region of Türkiye
AU - Deniz Arca
PY - 2026
DA - 2026/09/01
JO - Scientific Reports
DO - 10.1038/s41598-026-72166-x
UR - https://doi.org/10.1038/s41598-026-72166-x
AB - Understanding spatial variations in climate is essential for evaluating water balance, land use, and regional sustainability. This study proposes a GIS-based multi-method framework that integrates classical climate classification systems—Thornthwaite, de Martonne, Erinç, Köppen, Köppen–Geiger, and Trewartha—and employs a comparative geostatistical interpolation approach to delineate climate boundaries in an Aegean coastal region of Türkiye. Long-term climate-normal data (1991–2021) extracted from Climate-Data.org for four representative locations were used for climate classification analyses, whereas data from all nine geographically distinct locations were incorporated into the geostatistical interpolation to generate spatially continuous climate maps. The Kruskal–Wallis test was applied to evaluate differences in potential evapotranspiration (ETp) among stations, showing no statistically significant variation (p = 0.797). Among the six evaluated interpolation methods, Radial Basis Function (RBF) achieved the lowest prediction errors (RMSE = 4.342; R² = 0.758), while Empirical Bayesian Kriging (EBK) showed comparable performance (RMSE = 4.590; MAE = 3.271; R² = 0.729). Although RBF yielded slightly better cross-validation metrics, EBK was selected for spatial climate mapping due to its ability to explicitly model spatial autocorrelation and quantify prediction uncertainty, which is particularly advantageous under sparse and uneven station distribution. Thornthwaite precipitation effectiveness index (Im) values ranged from − 9.81 to 9.49, indicating predominantly dry to subhumid climatic conditions across the study area. The proposed GIS-based framework demonstrates that integrating multiple climate classification systems with comparative interpolation analysis improves the delineation of regional climate boundaries and provides a transferable methodology for climate mapping in data-limited coastal environments.
SN - 2045-2322
ER -