Assessment of future hydrometeorological variability in the Konya Basin, Türkiye using bias-corrected CMIP6 projections
Journal of Atmospheric and Solar-Terrestrial Physics, cilt.285, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 285
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.jastp.2026.106885
- Dergi Adı: Journal of Atmospheric and Solar-Terrestrial Physics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Artic & Antarctic Regions, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Bias correction, Climate change, CMIP6, Hydrometeorological projections, Konya Basin, Quantile mapping, SSP scenarios, Universal Co-Kriging
- Akdeniz Üniversitesi Adresli: Evet
Özet
This study presents bias-corrected projections of soil moisture, precipitation, Temperature and evapotranspiration in the Konya Basin, derived using CMIP6 data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Bias correction methods, including Delta, Quantile Mapping, and Empirical Quantile Mapping, were applied to the raw model outputs. Historical data from CMIP6 (1950–2014) and future projections (2015–2100) were corrected using ERA5 reanalysis data (1940–2024) to improve accuracy. Nine global climate models ACCESS-CM2, CESM2, CMCC-ESM2, EC-Earth3-CC, MIROC6, GFDL-ESM4, MPI-ESM1-2-LR, MRI-ESM2-0, and NorESM2-LM were evaluated, and ACCESS-CM2 provided the most reliable results for all three SSP scenarios. The data were divided into four time periods: the reference period (2015–2024), near future (2025–2049), mid-future (2050–2074), and far future (2075–2100). Relative errors were calculated as (Observed - Predicted)/Observed × 100, with temperature trends incorporated for comparative assessments. For spatial analysis, Universal Co-Kriging was employed to generate predictive maps for each future period, using elevation as a secondary variable. Therefore, the Universal Co-Kriging parameters determined through a trial-and-error approach namely, the use of a Gaussian kernel, standard neighborhood structure with 5 maximum and 2 minimum neighbors, and a 45-degree sectoral division in four directions proved to be the most effective combination for accurately mapping the spatial distribution of soil moisture, precipitation, temperature, and evapotranspiration in the Konya Basin. The study provides valuable insights into the future variability of hydrometeorological variables in the Konya Basin, offering critical information for water resource management and climate adaptation strategies.