Evaluation Of Meteorological Drought Using Multiple Indices: A Case Study Of Borana And Guji Zones, Ethiopia

Abstract

Now a day, extreme events like drought have obtained momentary attention in Ethiopia due to their adverse consequences. Particularly, drought has affected larger areas in Borena and Guji zones. However, to the author’s knowledge, the extent of such impacts was not compressively addressed as it is vital for early warning and adaptation measures. Therefore, this study focuses on evaluating the meteorological drought in Borana and Guji zones using multiple indices. The spatial pattern of drought was evaluated using both ground climate-based indices such as (SPI and SPEI), and remote sensing-based indices, namely, NDVI, VCI and LST. Multi-temporal satellite data from various Landsat series including Landsat5 (Thematic Mapper) (TM), Landsat7 Enhanced Thematic Mapper(ETM+), and Landsat 8(OLI/TIRS) were downloaded from the United States Geological Survey (USGS) platform. Climate-based drought indices were generated using R programming version 4.2.0 while satellite imageries pre-processing, analysis and classification were done using Earth Resource Data Analysis System (ERDAS )2015 software. The maximum SPI value found to be -3.31538 in spring season, -2. 71088.in Autumn season, -3.79724, in annual season during the year2017,2020, and 2000 respectively. And maximum SPEI was Found to be - 2.93809 in spring season, -2.96911 in autumn season and-2.93911 in annual season during the year 1992,2000 and 2020 respectively. Mann Kendall trend test was done to determine trend changes in meteorological based drought indices Similarly, from NDVI In 2020 year, an area of 11640km2 was affected by extreme drought and 33240.23km2 was affected by severe drought. The VCI of the area was also calculated and the result from VCI shows the area was highly affected by meteorological drought. Additionally, a result from LST shows there is an increase in temperature from 1990 to 2020 in the study area. The Pearson correlation coefficient between NDVI and SPI was(r2 ) = 0. 872.This correlation value shows that there is the strongest correlation between these two indices. In short, the present findings can be used as benchmarking information for disaster risk managers, environmentalists, meteorologist decision-makers in mainstreaming the adverse consequences of drought and plan proper adaption strategy(ies).