DROUGHT RISK ASSESSMENT USING REMOTE SENSING AND GIS A CASE STUDY IN ABAYA DISTRICT, ETHIOPIA

Abstract Drought is the most complex but least understood of all natural hazards. Timely information about the onset of drought, extent, intensity, duration and impacts can limit drought related ix losses of life, human suffering and decrease damage to economy and environment. In this study an attempt has been made to assess drought using RS and GIS techniques in West Guji Zone, Abaya district. Standardized Precipitation Index (SPI) was used to determinedrought in the study area.Rainfall data of study area and around the district were used and interpolated to determine the spatial pattern of meteorological drought and threshold (average) value for different classification types of drought through SPI. Agricultural drought risk areas were identified based on Normalized Difference Vegetation Index (NDVI)anomaly by using vegetation index data set with 250m resolution from MODIS satellite image from 2004-2018, because the Landsat image with resolution of 30m was affected by strip line and cloud of some year, so it’s not incredible to use for study.NDVI anomalyfrom the mean values was typically classified to determine the agricultural drought risk area.The spatial patterns of agricultural drought events and the levels of its severity ranged from slight in most of the years to severedrought in 2015 and 2010. Generally, the extent of severe drought coverage, registered in this case, stretched areas of central, western and in small pocket of south eastern parts of the study area and the study area was stricken by sever (11.34 %) moderate (46.8%), slightly (37.5%)and no drought (4.36 %)agricultural drought risk map. Meteorological drought risk map indicated that the study area has been affectedby extreme sever (58%), sever (27%), moderate(9.36%)and slight(5.64%) meteorological drought. From the above result the satellite developed indices based on the ground data is vital for successful drought assessment and identification of drought vulnerable areas.