| dc.description.abstract | Somalia is one of the most drought prone countries in Africa. Drought is the country’s costliest natural disaster. The impact of drought events on the economy, on people`s livelihoods and on lives has grown. Drought events usually develop gradually unnoticed, causing tremendous effects on both agriculture and environment. This study evaluated the spatio-temporal variations of drought occurrences in Somalia and its implication on forest cover. In this study, precipitation and temperature variables were taken as a proxy to assess and quantify long-term drought in Somalia. The intensities, frequencies and trends of drought occurrences were analyzed using Standardized Precipitation Evapotranspiration Index for the multiple timescales of 1-, 3-, 6-, 12-, and 24-month. Drought maps were done using Normalized Difference Vegetation Index during the period 1982 to 2015. Hansen data was used to account for forest cover change whereas a sample of 20 papers were reviewed in order to determine the best fit land degradation mitigation measures. Temporal variations in drought showed decreasing trends in severity and increasing trends in drought duration as the SPEI timescales increased. The major drought event as identified by SPEI 12 occurred during the period between May 2011 and January 2013, lasting for a period of 12months with an intensity of -0.55. Furthermore, drought count was generally high in the southern parts of Somalia and entire country had suffered droughts ranging from moderately dry to severely dry conditions. An average drought duration of between 2 and 3 months was recorded for most (81%) of the locations. About 23 % of forest cover was lost from 79,294 ha to 67,199ha from 2000 to 2019 representing a loss of 1,058ha per year. The study findings show the need for immediate actions to tackle drought and hence poverty and famine in Somalia. A combination of remote sensing tools conservation practices (such as agroforestry, afforestation, reforestation and conservation agriculture) is ideal for monitoring and mitigating land degradation effects respectively. | en_US |
