Climate change impacts on the water cycle can severely affect regions that rely on groundwater to meet their water demands in the mid- to long-term. In the Lake Tana basin, Ethiopia, discharge regimes are dominated by groundwater. We assess the impacts of climate change on the groundwater contribution to streamflow (GWQ) and other major water balance components in two tributary catchments of Lake Tana. Based on an ensemble of 35 bias-corrected regional climate models and a hydrologic catchment model, likely changes under two representative concentration pathways (RCP4.5 and 8.5) are assessed. No or only slight changes in rainfall depth are expected, but the number of rainy days is expected to decrease. Compared to the baseline average, GWQ is projected to decrease whereas surface runoff is projected to increase. Hence, rainfall trends alone are not revealing future water availability and may even be misleading, if regions rely heavily on groundwater.

The Lake Tana basin hosts more than three million people and it is well known for its water resource potential by the Ethiopian government. The major economic activity in the region is agriculture, but the effect of agricultural crops on water resources is poorly understood. Understanding the crop water interaction is important to design proper water management plans. Therefore, the primary objective of this research is to investigate the effect of different agricultural crops on the spatial and seasonal variability of water balance components of Gilgelabay, Gumara, and Ribb catchment areas of Lake Tana basin, Ethiopia. To this end, the hydrologic model SWAT (Soil and Water Assessment Tool) was used to simulate the water fluxes between 1980 and 2014. The water balance components, which were mapped for each hydrologic response unit, indicated the spatial variations of water fluxes in the study. Cereal crops like teff and millet had significant effect in enhancing groundwater recharge, whereas leguminous crops like peas had significant impact in increasing runoff generation. Moreover, the model outputs showed that the total streamflow is dominated by baseflow and about 13%, 9%, and 7% of the annual rainfall goes to the deep aquifer system of Gilgelabay, Gumara, and Ribb catchment areas, respectively.

A proper understanding of hydrological processes is vital for water resource assessment, management, and conservation at a local, national, and global scale. The role of hydrological models is critically important in rarely studied ungauged catchments including of Kobo-Golina, in the Danakil basin of Ethiopia. The main objective of this research is to model the hydrology of the Kobo-Golina catchment using the completely restructured SWAT (SWAT+) model. Validated reanalysis river flow from the Global Flood Awareness System (GloFAS) and actual evapotranspiration (AET) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for single and multi-variable calibration. It is found that the multi-variable calibration scenario reasonably attained the minimum satisfactory performance limit for both variables (NSE = 0.67, R² = 0.68, PBias = −9.68%, and RSR = 0.57 for calibration of GloFAS flow; and NSE = 0.56, R² = 0.63, RSR = 0.66, PBias = 3.86 for calibration of MODIS AET). The model simulation showed that evapotranspiration accounts for 47% of the input water while surface runoff, lateral flow, and groundwater recharge account for 30%, 1.53%, and 21.4%, respectively. The simulated mean annual streamflow at the Basin outlet is 10.6 m³/s. The monthly low flow occurs in June with a median flow of 1.43 m³/s and a coefficient of dispersion of 0.67. High flows occur in August, with a median flow of 16.55 m³/s and a coefficient of dispersion of 1.55. The spatial distribution of simulated runoff was depicted as being higher in the floodplains and along the riparian/drainage lines, whereas upland areas showed lower runoff. The maximum monthly recharge occurs in September with a recharge value of 78.2 mm. The findings of the study suggested that both surface water harvesting and groundwater exploitation can be sought in floodplain areas while conserving the uplands.

This research focuses on the statistical analyses of hydrometeorological time series in the basin of Lake Tana, the largest freshwater lake in Ethiopia. We used autocorrelation, cross-correlation, Mann–Kendall, and Tukey multiple mean comparison tests to understand the spatiotemporal variation of the hydrometeorological data in the period from 1960 to 2015. Our results show that mean annual streamflow and the lake water level are varying significantly from decade to decade, whereas the mean annual rainfall variation is not significant. The decadal mean of the lake outflow and the lake water level decreased between the 1990s and 2000s by 11.34 m³/s and 0.35 m, respectively. The autocorrelation for both rainfall and streamflow were significantly different from zero, indicating that the sample data are non-random. Changes in streamflow and lake water level are linked to land use changes. Improvements in agricultural water management could contribute to mitigate the decreasing trends.

Process-based hydrologic models can provide necessary information for water resources management. However, the reliability of hydrological models depends on the availability of appropriate input data and proper model calibration. In this study, we demonstrate that common calibration procedures that assume stationarity of hydrological processes can lead to unsatisfactory model performance in areas that experience a strong seasonal climate. Moreover, we develop a more robust calibration procedure for the Soil and Water Assessment Tool (SWAT) in the Adyar catchment of Chennai, India. Calibration was carried out based on seasonal decomposition and by successively shifting the calibration period. Daily and monthly streamflow records were used to investigate how these different calibration procedures influence model parameterization. Results show that SWAT model performance improved when calibrated after separating the streamflow into wet and dry seasons. The wet season calibration increased the Kling Gupta Efficiency coefficient and Nash–Sutcliffe Efficiency coefficient values from 0.56 to 0.68 and 0.19 to 0.51, respectively, compared to calibration based on wet and dry seasons together. In addition, when calibration time periods were shifted, resultant sets of model parameter values and performance metrics differed. Calibration based on the 2004–2009 period resulted in an overestimation of streamflow by 8.2%, whereas the overestimation was 12.1%, 18.3%, and 20.0% for the 2004–2010, 2004–2011, and 2004–2012 periods, respectively. This study underlines that both the availability of observed streamflow data and the way these data are applied to calibration have a strong impact on model parameterization and performance.