Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia.

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Evaluation of semi-distributed hydrological models performance in borkena watershed; upper awash basin, Ethiopia.

Flood is one of the most significant disasters in human life and economic destruction. To challenge this disaster, the use of models is very important to predict the magnitude and impact of river flow and to find a solution of the problems. This research is aimed to compare the performance of semi-distributed hydrological models in the Borkena watershed. The selected semi-distributed hydrological models were soil and water assessment tool (SWAT), hydrological engineering center-hydrologic modeling system (HEC-HMS), hydrologiska byråns vattenbalansavdelnin (HBV), and parameter efficient distribution (PED). The models were calibrated from 1999 to 2009 and validated from 2010 to 2015 using daily data. Based on validation results; The Nashsutclif (NSC) output of the SWAT, HEC-HMS, HBV, and PED models were 0.68. 0.66, 0.65, and 0.65, coefficient of determination (R2) 0.69, 0.67, 0.71, and 0.70, percentage of bias (PBIAS) -6.5, 0.6, 27.34, and 10.28, and root mean square error (RMSE) 14.24, 17.45, 17.63 and 0.91, respectively. Based on the models' performance results in Borkena watershed, the first effective model was SWAT and the second one was HEC-HMS. The HBV and PED models took third and fourth places respectively. The overall results show that the two infiltration excess models (SWAT and HEC-HMS) were performed in a better way than the two saturation excess models (HBV and PED) on this watershed. Therefore, according to the model output, the Borkena watershed is an infiltration excess area.

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia.

The objective of this study is to investigate and perform long-term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Comparing surface water supply index and streamflow drought index for hydrological drought analysis in Ethiopia.

Recently, floods and drought have become common natural hydroclimatic hazards in several countries. Consequently, the identification of an appropriate drought index is now a challenging task for researchers. It is obvious that there is not a single best drought index; rather a comparison of indices will give a relative option. The objective of this study was to compare two hydrological drought indices; the modified surface water supply index (M1SWSI) and streamflow drought index (SDI) over eight river basins, in Ethiopia. The M1SWSI and SDI value was computed from 1973 to 2014 using 34 streamflow stations, 42 rainfall gauge stations, and 3 lake-level data. The two indices results showed that the 1980s were the most severe drought years for all river basins. But for the case of Genale Dawa and Wabishebele basins, the drought severity increased from 2000 to 2014. Hydrological drought analysis using SDI has more drought occurrence frequency than M1SWSI. In all river basins from 1973 to 2014, there were a total of 18 severe drought events when using M1SWSI, but there were a total of 39 severe and 12 extreme drought events when using SDI. This implied that M1SWSI reduced the occurrence probability of severe drought by 53.85% and extreme drought by 100%. It is known that Ethiopia is stricken by extreme droughts in the last few decades. But M1SWSI doesn't detect those invidious drought events. In this study, SDI is found to be a better hydrological drought index. Therefore, policy and strategic planners, master plan developers, and decision-makers can use SDI to analyze historical and future hydrological drought trends to develop effective drought mitigation measures.