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<p>Natural disasters like droughts and flood events have frequently been occurring due to climate change in the global pattern of precipitation in recent decades. Estimating the future spatiotemporal precipitation variability is necessary to mitigate climate change's impact, particularly extreme precipitation events. However, global and regional climate models typically vary on the projected change in precipitation characteristics over particular regions. Therefore, this study comprehensively evaluates historical and future climate models in terms of spatial distribution, annual cycles, and frequency distributions of precipitation over the Blue Nile basin (BNB) based on different statistical indices. Also, the autocorrelated time series data were subjected to the Mann-Kendall (MK) and Sen's slope estimator tests to identify trends. Many regional and global climate models, such as HadCM3, ECHAM5, MPI-ESM-LR, and EC-EARTH, are employed not only better to understand the discrepancy and the uncertainties of climate models but also to estimate the impact of climate change in the extreme precipitation events over the Blue Nile basin (BNB). Overall, our finding would serve as a benchmark for flood risk mitigation research and water resources management applications over the Blue River basin.</p>

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Because of the sparseness of the ground monitoring network, precipitation estimations based on satellite products (PESPs) are currently requisite tools for hydrological simulation research and applications. The evaluation of six global high-resolution PESPs (TRMM 3B42V7, GPGP-1DD, TRMM 3B42RT, CMORPH-V1.0, PERSIANN, and PERSIANN-CDR) is the ultimate purpose of this research. Additionally, the distributed Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) is used to investigate their potential effects in streamflow predictions over the Blue Nile basin (BNB) during the period 2001 to 2007. The correctness of the studied PESPs is assessed by applying categorical criteria to appraise their performances in estimating and reproducing precipitation amounts, while statistical indicators are utilized to determine their rain detection capabilities. Our findings reveal that TRMM 3B42V7 outperforms the remaining product in both the estimation of precipitation and the hydrological simulation, as reflected in highest NSE and R2 values ranges from 0.85 to 0.94. Generally, the TRMM 3B42V7 precipitation product exhibits tremendous potential as a substitute for precipitation estimates in the BNB, which will provide powerful forcing input data for distributed hydrological models. Overall, this study will hopefully provide a better comprehension of the usefulness and uncertainties of various PESPs in streamflow simulations, particularly in this region.

<p>Data availability and accuracy is predominantly an issue for building hydrological applications, particularly in data-scare regions, like Africa. This is further one of the challenges that hinders understanding the climate variability and its subsequent extreme flood and drought events. Forcing data from different sources, e.g., satellite sensors, in-situ observations, or reanalysis products, are required to derive hydrological models. Reanalysis products have recently become an alternative tool of meteorological data given their long record at various temporal and spatial scales. The overarching goal of this study is to evaluate the primary forcing data for hydrological models; precipitation, as produced by six different reanalysis data (JRA55, 20CRv3, ERA5, ERA-20C, MERRA, NCEP/NCAR). We here focused our evaluation on the major river basins in Africa during a 15-year period spanning from 2001 to 2015. The five major river basins include the Nile River, Congo River, Zambezi River, Orange River, and Niger River basins. Our evaluation method is summarized as follows: Firstly, precipitation data is compared with the gridded gauged data, e.g., CHIRPS for precipitation. Secondly, statistical indices, including categorical and continuous statistical metrics, will be used to assess the accuracy of reanalysis products over each of the major basins. Finally, we present the intercomparison of reanalysis products for extreme events including floods and droughts. The results from our evaluation will pinpoint the skill of reanalysis products and thus benefit the future development of hydrological modeling over the river basins in Africa.</p>