Multi-Criteria Decision Framework to Evaluate Bias Corrected Climate Change Projections in the Piracicaba River Basin

Abstract Regional climate models (RCM) are the main tools for change impacts assessment in hydrological studies. These models, however, often show biases when compared to historical observations. Bias Correction (BC) useful techniques improve projection outputs. This study presents a multi-criteria decision analysis (MCDA) framework compare combinations of RCM with selected BC methods. The comparison was based on modified Kling-Gupta efficiency (KGE’). criteria evaluated general capability reproducing observed data statistics. Other were relevant aspects studies, such as seasonality, dry and wet periods. We applied four methods monthly rainfall outputs from 1961 2005 Piracicaba river basin. Linear Scaling (LS) method showed higher improvements performance models. Eta-HadGEM2-ES, corrected Standardized Reconstruction (SdRc) method, achieved best results precipitation. bias projected precipitation (2006-2098) preserved signal effects original regarding annual rainfall. However, SdRc produced significant decrease average rainfall, than 45% July, August September RCP4.5 RCP8.5 scenarios.