Gis And Remote Sensing Based Climate Variability Assessment In Shashogo District, Southern Ethiopia

Abstract

Ethiopia has historically been hurt by climate variability and extremes. The influence of climate variability is high in Ethiopia. The study aimed at geographical information system (GIS) and remote sensing-based climate variability assessment in Shashogo District, southern Ethiopia. In this study, blended/gridded time series data of rainfall and temperature data for thirty years (1989-2018) were collected from Ethiopia National Meteorological Institute (NMI). Moreover, Landsat images of 1989TM, 2005ETM+, and 2021 OLI with row 55, and path 169 have been used in the present study. Normalized Difference Vegetation Index (NDVI) data from the National Aeronautics and Sp ace Administration [NASA] of LANDSAT_5 and LANDSAT_8 were utilized. The household survey was carried out to verify the output of NMI and remote sensing-based satellite images. Due to the homogenous nature of the population, a total of 114 respondents and two(2) key informants were involved in the study. Rainfall and temperature variability were analyzed using standard de viations (SD), coefficient of variation (CV), rainfall anomaly index (RAI), standard precipitation index (SPI), and precipitation concentration index (PCI). Sen’s slope estimator, and Mann–Kendall test was used to check the statistical significance of the trends. The results revealed that there is a de creasing trend of rainfall between 1989 and 2018. Late-onset and early cessation of rainfall have been detected in the study area. The result showed that the maximum temperature increased by 0.06420C/year between the year 1989 and 2018). The minimum temperature also experiences an increasing trend which indicates that climate variability is persistent, particularly in the small rainy season ‘belg’. The decline in rainfall can affect vegetation conditions and crop production. St atistical correlation analyses showed that there is a moderate positive correlation between NDVI and rainfall. On the opposite, a negative correlation was found between temperature and NDVI. The land use land cover classification results showed that the forest cover is significantly declining over the study period. Accordingly, the forest cover has declined from 74.2km2 (20.8%) in 1989 to 15.6km2(4.24%) in 2021 with a total loss of 59.14km2 (16.56%). Generally, the time series analysis result reflected that rainfall, minimum temperature, and maximum temperatures observed in the study area have revealed a clear variation that contributed to the present climate dynamics in the locality. Thus, the study recommends that educating the local community on how to mediate the problem happening and developing various mechanisms on how to protect their environment is very crucial. The study also suggests that the local government has to do with its community to recover the existing and to restore the disappeared forest resources.