| dc.description.abstract | The recent droughts that occurred in different parts of Ethiopia have been generally linked to changes in patterns in atmospheric and ocean circulation. The occurrence of drought has intensified with the unprecedented impacts of climate change. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is essential. Knowledge about the spatiotemporal distribution of droughts and trends is vital for risk management and developing adaptation and mitigation strategies.
In this study, several techniques and datasets were analyzed to study the Spatio-temporal variability of vegetation in response to a changing climate. Eighteen 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 Vegetation Condition Index (VCI) was also used to assess drought patterns during the cropping season.
Moreover, we have developed seven Artificial Neural Network (ANN) predictive models that incorporate 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 input parameters that trigger droughts and measure their predictive ability by comparing the predicted with the observed values.
The results indicate that the central highlands and northwestern areas of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI. 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 decrease in NDVI, especially in the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the 2009 and 2015 El Niño years. Further, correlation coefficient analysis shows that the low NDVI was mainly related to the limited precipitation and reduced 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. This study presents the results of the first attempt to apply a recently developed index, Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results showed that NDLI was highly correlated with NDVI (r=0.96), precipitation (r=0.81), Soil moisture (r=0.73) and LST ( r= -0.67). NDLI successfully captured historical droughts and has a notable correlation with the climatic variables. The analysis showed that using the Green, Red, and SWIR, a simplified crop monitoring model with satisfying accuracy and easiness can be developed.
The statistical comparisons of the different ANN models showed accurate results in predicting SPEI values that can be achieved by including large-scale climate indices. It was found that the coefficient of determination and root-mean-square error of the best-fit models ranged from 0.820-0.949, 0.263-0.428, respectively. The ANN models used here offer an alternative framework for forecasting the SPEI drought index. | en_US |