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 rain 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 heavily rely on groundwater.

The Ethiopian government has selected Lake Tana basin as a development corridor due to its water resources potential. However, combined use of groundwater (GW) and surface water (SW) is still inadequate due to knowledge gaps about the flow dynamics of GW and SW. Mostly, there is no information about groundwater use. Therefore, this study aims to investigate the dynamics of GW-SW interactions on a spatio-temporal basis in three of the main catchments (Gilgelabay, Gumara and Ribb) that drain into Lake Tana. To this end, the SWAT-MODFLOW model, which is an integration of SWAT (Soil and Water assessment Tool) and MODFLOW, is used. The results reveal strong hydraulic connection between the GW and SW in all the three catchments. In the Gilgelabay catchment, the flow from the aquifer to the river reaches dominates (annual discharge from the aquifer varies from 170 to 525,000 m3/day), whereas in Gumara (annual exchange rate between −6,530 and 1,710 m3/day) and Ribb (annual exchange rate between −8,020 and 1,453 m3/day) the main flow is from the river reaches to the aquifer system. The flow pattern differs in the three catchments due to variations of the aquifer parameters and morphological heterogeneity. Overall, this study improves our understanding of GW-SW flow dynamics and provides insights for future research works and sustainable water management in the Nile region.

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.

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.

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.