For the selection of global climate models in the upper basin of the Blue Nile, an advanced envelope-based approach was used. Currently, the number of general circulations models (GCM) has increased extremely. The reliability of any general circulation model in a particular region is confronted, so the selection of the appropriate climate models that can predict the climate variable is essential. Representative concentration pathways RCP4.5 and RCP8.5 were taken into account. For RCP4.5 105 GCMs were used and for RCP8.5 78 GCMs were used to select the best performance models for the Upper Blue Nile Basin for a climate change impact study. Three steps were followed to derive the best performing models in the study area based on their range of projected mean temperature and precipitation changes, the range of projected extreme changes, and the ability to reproduce past climates between 1971 and 2000 and 2071–2100. Five corners of the spectrum were used, e.g., wet-warm, wet-cold, dry-warm, dry-cold, and the 50th percentile of the temperatures. For RCP4.5 and RCP8.5, a total of 25 GCMs were chosen based on the range of anticipated mean temperature and rainfall change. Based on the range of extreme changes, 10 GCMs were chosen. Finally, for each RCP4.5 and RCP8.5, five GCMs were chosen by combining all three stages.
This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used as Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods such as Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM) on five metrological stations. Six statistical performance measuring techniques were employed. The evaluation was carried out from the year 2003 to 2016 on daily and monthly time scales. The results depicted that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study highlights the benefits of using SRPs and bias correction methods to enhance the distribution of local rainfall data, which is critical for water resource planning and other related sectors.
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