Projecting aridity from statistically downscaled and bias-corrected variables for the Gediz Basin, Turkey

Abstract

Due to climatological changes, a study was conducted in the Gediz Basin, Turkey, where agricultural production holds an important place. In the study prepared, 12 general circulation models (GCMs) were utilized under representative concentration pathway (RCP)4.5, RCP6.0, and RCP8.5 scenarios of the fifth assessment report (AR5) of IPCC for the period 2015–2050. The statistical downscaling methods were employed and the projections were derived right after applying the weighted-averaged ensemble mean by the Bayesian Model Averaging (BMA) method and bias correction by equidistant quantile mapping (EDQM). The temperature-based potential evapotranspiration (PET) formulas were modified in accordance with the Penman–Monteith method and the aridity indexes were calculated by UNEP's formula. According to the projections, the mean annual temperature increases between 1.5 and 2.2 °C and the mean total annual PET increases between 5 and 8% are foreseen in the Gediz Basin for the near future. It is foreseen that a semi-arid climate regime may predominate over the region for all of the RCP scenarios under the increasing dryness in basin climate. In addition, it was obtained in the study that sub-humid climate state occurrence for all of the regions included by the basin may be unexpected in the future for the RCP8.5 scenario. The presence of semi-arid climate conditions may be more potent with the increasing trend of radiative forcing over time. HIGHLIGHTS Artificial intelligence methods were performed for downscaling.; A multi-model ensemble strategy was carried out for climate projection.; A robust bias-correction method was utilized.; Climate change forecasts were integrated with aridity concepts.; Spatio-temporal aridity changes were presented for the future term.;

Keywords

• aridity index
• bayesian model averaging
• equidistant quantile mapping
• statistical downscaling