Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998-2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years

An experimental drought monitoring tool has been developed that predicts the vegetation condition (Vegetation Outlook) using a regression-tree technique at a monthly time step during the growing season in Eastern Africa. This prediction tool (VegOut-Ethiopia) is demonstrated for Ethiopia as a case study. VegOut-Ethiopia predicts the standardized values of the Normalized Difference Vegetation Index (NDVI) at multiple time steps (weeks to months into the future) based on analysis of ''historical patterns'' of satellite, climate, and oceanic data over historical records. The model underlying VegOut-Ethiopia capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation (ENSO)) expressed over the 24 year data record and also considers several environmental characteristics (e.g., land cover and elevation) that influence vegetation's response to weather conditions to produce 8 km maps that depict future general vegetation conditions. VegOut-Ethiopia could provide vegetation monitoring capabilities at local, national, and regional levels that can complement more traditional remote sensing-based approaches that monitor ''current'' vegetation conditions. The preliminary results of this case study showed that the models were able to predict the vegetation stress (both spatial extent and severity) in drought years 1-3 months ahead during the growing season in Ethiopia. The correlation coefficients between the predicted and satellite-observed vegetation condition range from 0.50 to 0.90. Based on the lessons learned from past research activities and emerging experimental forecast models, future studies are recommended that could help Eastern Africa in advancing knowledge of climate, remote sensing, hydrology, and water resources.

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