Downscaling of general circulation model (GCM) outputs extracted from CMIP5 datasets to monthly precipitation for the Gediz Basin, Turkey, under Representative Concentration Pathways (RCPs) was performed by statistical downscaling models, multi-GCM ensemble and bias correction. The output databases from 12 GCMs were used for the projections. To determine explanatory predictor variables, the correlation analysis was applied between precipitation observed at 39 meteorological stations located over the Basin and potential predictors of ERA-Interim reanalysis data. After setting both artificial neural networks and least-squares support vector machine-based statistical downscaling models calibrated with determined predictor variables, downscaling models producing the most suitable results were chosen for each meteorological station. The selected downscaling model structure for each station was then operated with historical and future scenarios RCP4.5, RCP6.0 and RCP8.5. Afterwards, the monthly precipitation forecasts were obtained from a multi-GCM ensemble based on Bayesian model averaging and bias correction applications. The statistical significance of the foreseen changes for the future period 2015-2050 was investigated using Student's t test. The projected decrease trend in precipitation is significant for the RCP8.5 scenario, whereas it is less significant for the RCP4.5 and RCP6.0 scenarios.
Statistical downscaling models are very effective tools for downscaling coarseresolution climate models to local scale and are widely used in climate change studies. The different climate models used in the projections of various hydro-meteorological variables affect the performance of the downscaling models due to their inherent bias and can reduce the precision of predictions. Due to this reason, bias correction methods are needed in addition to the downscaling models. In the study prepared, the precipitation projections were obtained by the climate models derived within the framework of different emission scenarios in terms of the 5th Assessment Report of Intergovernmental Panel on Climate Change (IPCC) and the effects of different bias correction methods on precipitation estimations were investigated as well. For this purpose, firstly, the predictor selection which represents the precipitation of Gediz Basin was carried out and then the coarse-resolution climate models were downscaled to station scale by means of the related precipitation predictors. In the study, 12 different global climate models having raw outputs of 2015-2050 future period were utilized and it was aimed to obtain stronger predictions by combining the projections which were derived by these climate models. Subsequent to combination of multi-model projections, the bias existing in predictions were corrected by Quantile Mapping (QM), Equiratio Quantile Mapping (ERQM), Detrended Quantile Mapping (DQM) and Quantile Delta Mapping (QDM), respectively. According to the obtained results including all performance measures, it has been deduced that QM offers the largest error values. On the other side, it has been concluded that QDM method can better reflect relative changes compared to other methods. When performance indices pointing out extreme processes were also investigated, it was observed that QDM was superior in the evaluation of mean-based precipitation projections.
In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall–runoff models (dynamic water balance model and abcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence speed. A variance decomposition-based method – analysis of variance – was also used to quantify the dynamic sensitivity of HPSO parameters. Accordingly, the individual and interactive uncertainties of the parameters defined in the HPSO are relatively low.
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