The recent droughts that have occurred in different parts of Ethiopia are generally linked to fluctuations in atmospheric and ocean circulations. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is important. In view of this, several techniques and datasets were analyzed to study the spatio–temporal variability of vegetation in response to a changing climate. In this study, 18 years (2001–2018) of Moderate Resolution Imaging Spectroscopy (MODIS) Terra/Aqua, normalized difference vegetation index (NDVI), land surface temperature (LST), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) daily precipitation, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) soil moisture datasets were processed. Pixel-based Mann–Kendall trend analysis and the Vegetation Condition Index (VCI) were used to assess the drought patterns during the cropping season. Results indicate that the central highlands and northwestern part of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI trends. About 52.8% of the pixels showed a decreasing precipitation trend, of which the significant decreasing trends focused on the central and low land areas. Also, 41.67% of the pixels showed a decreasing NDVI trend, especially in major parts of the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the El Niño 2009 and 2015 years. Furthermore, the Pearson correlation coefficient analysis assures that the low NDVI was mainly attributed to the low precipitation and water availability in the soils. This study provides valuable information in identifying the locations with the potential concern of drought and planning for immediate action of relief measures. Furthermore, this paper presents the results of the first attempt to apply a recently developed index, the Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results show that the NDLI has a high correlation with NDVI (r = 0.96), precipitation (r = 0.81), soil moisture (r = 0.73), and LST (r = −0.67). NDLI successfully captures the historical droughts and shows a notable correlation with the climatic variables. The analysis shows that using the radiances of green, red, and short wave infrared (SWIR), a simplified crop monitoring model with satisfactory accuracy and easiness can be developed.

The occurrence frequency of drought has intensified with the unprecedented effect of global warming. Knowledge about the spatiotemporal distributions of droughts and their trends is crucial for risk management and developing mitigation strategies. In this study, we developed seven artificial neural network (ANN) predictive models incorporating hydro-meteorological, climate, sea surface temperatures, and topographic attributes to forecast the standardized precipitation evapotranspiration index (SPEI) for seven stations in the Upper Blue Nile basin (UBN) of Ethiopia from 1986 to 2015. The main aim was to analyze the sensitivity of drought-trigger input parameters and to measure their predictive ability by comparing the predicted values with the observed values. Statistical comparisons of the different models showed that accurate results in predicting SPEI values could be achieved by including large-scale climate indices. Furthermore, it was found that the coefficient of determination and the root-mean-square error of the best architecture ranged from 0.820 to 0.949 and 0.263 to 0.428, respectively. In terms of statistical achievement, we concluded that ANNs offer an alternative framework for forecasting the SPEI drought index.

HIGHLIGHTS. Our article is the first of its kind from an Ethiopian country, applying different techniques to quantify ocean–atmospheric interactions.Partic

In this study, we identified and investigated the annual climatological moisture sources for the Ethiopian river basins during 1980–2018. First, according to cluster analysis, the 12 river basins of this country were grouped into four regions: northeast (NE), southeast (SE), southwest (SW), and west (W), which were characterised by similar annual precipitation features. Global outputs from the Lagrangian FLEXPART model were used to investigate the air mass humidity gain before reaching each region. This revealed five main oceanic moisture sources located in the Mediterranean Sea, Red Sea, Indian Ocean, Persian Gulf, and the Arabian Sea, in addition to three main terrestrial moisture sources located in the African continent, Arabian Peninsula, and the regions themselves. Once the main climatological sources of moisture for each region were identified, a forward-in-time analysis of air masses over each source was performed to calculate the moisture contribution to precipitation (E – P) < 0 over the defined regions. The most important source at the annual scale for the NE, SW, and W regions is the African continent, while for the SE, it is the Indian Ocean. Indeed, terrestrial moisture sources are the major contributors (>50%) to the precipitation over the NE, SW, and W, whereas oceanic sources are the major contributors to the SE. Another analysis identified the years affected by drought conditions in the regions. The role of the sources was evaluated for those years affected by severe and extreme drought, revealing the heterogeneous and also direct influences on the regions. Finally, according to the normalised difference vegetation index, the impacts of annual severe and extreme droughts were more prominent in areas of the NE and SE, but also in the SW during 1984.