Comparison of different statistical downscaling models and future projection of areal mean precipitation of a river basin under climate change effect

Abstract

Investigation of the hydrological impacts of climate change at the local scale requires the use of a statistical downscaling technique. In order to use the output of a Global Circulation Model (GCM), a downscaling technique is used. In this study, statistical downscaling of monthly areal mean precipitation in the Göksun River basin in Turkey was carried out using the Group Method of Data Handling (GMDH), Support Vector Machine (SVM) and Gene Expression Programming (GEP) techniques. Large-scale weather factors were used for the basin with a monthly areal mean precipitation (PM) record from 1971 to 2000 used for training and testing periods. The R2-value for precipitation in the SVM, GEP and GMDH models are 0.62, 0.59, and 0.6 respectively, for the testing periods. The results show that SVM has the best model performance of the three proposed downscaling models, however, the GEP model has the lowest AIC value. The simulated results for the Canadian GCM3 (CGCM3) A1B and A2 scenarios show a similarity in their average precipitation prediction. Generally, both these scenarios anticipate a decrease in the average monthly precipitation during the simulated periods. Therefore, the results of the future projections show that mean precipitation might decrease during the period of 2021–2100. HIGHLIGHTS An integrated hydrological model for prediction of areal mean precipitation of a river basin under climate change effect is proposed.; Statistical downscaling and GCM are utilized to estimate the climate change effects on the basin.; Goksun River basin is used as the case-study area with three precipitation stations.; Simulated results anticipate a decrease in the average monthly precipitation during the period of 2021–2100.;

Keywords

• artificial intelligence
• climate change
• gcms
• precipitation
• statistical downscaling