This study aims to investigate trends and spatiotemporal variability of precipitation during the period of 1980-2015 in the Main Central Ethiopian Rift Valley Lakes Basin. Mann-Kendall (MK) and Sen’s Slope (SS) estimation were used to detect the trends and magnitudes respectively. The Inverse Distance Weighting method was employed for spatial interpolations. The results revealed that the rainfalls had experienced less concentrated and highly variable in the central rift floor. The trend analysis showed that out of 21 stations, 4 stations (Koshe, Bulbula, Kofole and Shashamane) in annual; six stations (Koshe, Bulbula, Tora, Wulberg, Wondogenet, and Shashamane) in spring; and only one station (Shashamane) in summer showed significantly decreasing trends with -4.5 to -15.59 mm/year range. All stations in spring rainfall revealed no positive trends. Conversely, in both annual and summer rainfall statistically significant increasing trends exhibited for Ejersalelle and Adamitulu with 6.4 to 7.94mm/year range. In monthly time scales, the significant decreasing and increasing trends were also investigated for a few stations. Increasing trends could lead to an increase in potential for water resources whereas variability and decreasing trends could boost over-exploitation of water resources. This study would be provided useful information for management of water resources in the study area.
The purpose of this study is to predict future changes in precipitation in the Central Ethiopian Main Rift, which is vulnerable to climate change. The Long Ashton Research Station Weather Generator (LARS-WG) model was applied to project precipitation based on five global climate models (GCMs) (EC-EARTH, MIROC-ESM, HadGEM2-ES, INM-CM4, and CCSM4) from Coupled Model Intercomparison Project phase 5 (CMIP5) under two representative concentration pathways (RCP4.5 and RCP8.5) in the periods of 2041–2060 compared to the baseline period of 1976–2005. The model's calibration and validation results showed that it could predict future precipitation. According to the analysis, the mean rainfall is expected to increase in January (up to 14.2%) and December (up to 27.8%) under the RCP 4.5 and RCP 8.5 scenarios, respectively. However, a drop is anticipated in June (up to 8.2%) and May (up to 7%) under the RCP 4.5 and RCP 8.5 scenarios, respectively. In both scenarios, summer precipitation (usually the rainy season) is predicted to fall, while winter precipitation (usually the dry season) is expected to climb. Furthermore, annual and spring precipitation forecasts are anticipated to decrease in most locations. The findings of this research will be utilized to guide future water resource management in the study region.
Droughts are defined by a prolonged absence of moisture. For making drought assessments, a drought index is a crucial tool. This study aims to compare drought characteristics across the Central Main Ethiopian Rift using three drought indices – the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), and the Standardized Precipitation Evapotranspiration Index – from 1980 to 2017 at six climate sites in spring, summer, and a 6-month period (March–August). With 1 and 5% significance levels, the modified Mann–Kendall and Sen's Slope estimators were used to determine trend and magnitude, respectively. The temporal fluctuations of the three drought indices revealed that droughts are frequent, unpredictable, and random. Furthermore, they behaved similarly and had significant links. At most places, the drought indices found no significant trends. However, in the spring season, Butajira (by the three indices) and Wulbareg (by the SPI) showed significantly decreasing trends (increasing drought severity), with change rates ranging from −0.03 to −0.04/year. A comparison of drought characteristics from 1980–1998 and 1999–2017 droughts have been more severe and frequent in recent decades, with spring being more prevalent than summer. This study, which employed a variety of drought indices, could assist water resource planners in better understanding drought events.
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