Assessing Climate Change Impact on Gilgel Abbay and Gumara Watershed Hydrology, the Upper Blue Nile Basin, Ethiopia

Abstract

Climate change and variability have significant influences on hydrological cycles and the availability of water in the Horn of Africa. Projections of six General Circulation Models (GCMs) in association with high (A2) and low (B1) emission scenarios were adopted in this study from the Special Report on Emission Scenarios (SRES) for the period 2020 -2039 to assess the impacts of climate changes on the Gilgel Abbay and Gumara watershed hydrology, the upper Blue Nile basin, Ethiopia. The GCMs selected were screened in accordance with baseline climate statistics of study areas. A weather generator was employed to generate daily temperature and precipitation to drive the General Water Loading Function (GWLF) hydrological model for simulating runoffs. Projected changes in temperature differences and precipitation ratios relative to the baseline were analyzed to explain the variations in evapotranspiration and the influences on runoff. Despite the fact that the projected magnitude varies among GCMs, increasing runoff in both wet and dry seasons was observed for both watersheds, attributable mainly to the increase in precipitation projected by most GCMs. In contrast to the great increases in runoff, variations in evapotranspiration are less significant. The projected runoff in both watersheds implies increased potential for promoting agricultural irrigation in the dry season. Furthermore, it would allow greater inflow to Lake Tana, the largest contributor to the Ethiopian Renaissance Dam on the Blue Nile. Therefore, concerned local, state, and federal government organizations shall be prepared to harness opportunities from the projected increase in runoff.

Modeling the Impact of Climate Change on Hydrological Responses in the Lake Tana Basin, Ethiopia

“…These results are in agreement with results from previous studies in the Lake Tana basin (Gebre and Ludwig 2015;Ayele et al 2016;Nigatu et al 2016;Melke and Abegaz 2017), though variations exist in the magnitude of change, which could be due to differences in GCM models, emission scenarios, and downscaling methods used between studies. However, this result contradicted with the study by Setegn et al (2011) in the Lake Tana basin who reported decreasing trends in future rainfall.…”

“…These results are comparable with the previous studies (Dile et al 2013;Nigatu et al 2016;Liersch et al 2018;Mekonnen and Disse 2018) in the Upper Blue Nile basin. Besides, the results agree with the study by Ayele et al (2016) in Gilgel Abay and Gumara watersheds. They reported that projected (2020-2039) temperature in the A2/B2 scenarios will increase by 1.2/1.1°C and 0.8/0.7°C in Gilgel Abay and Gumara watershed, respectively.…”

“…A decrease in seasonal rainfall in Gumara, Ribb, and Megech watershed resemble with the results by Beyene et al (2010), who also showed a decrease in the mean monthly rainfall in 2040-2069 in Nile river basin, where our study watersheds are located. Several researchers (Dile et al 2013;Gebre and Ludwig 2015;Ayele et al 2016) have shown that the streamflow and evapotranspiration were projected to increase in the major tributary rivers in the Lake Tana basin, and this is very similar to the results of this study. The seasonal streamflow variation mainly associated with seasonal rainfall variation.…”

“…They found that the mean monthly flow may decrease during the 2010-2040 period and increase during 2070-2100. Recently, Ayele et al (2016) used a stochastic weather generation approach to downscale climate data from six General Circulation Models (GCMs) for the high (A2) and low (B1) emission scenarios in the Gilgel Abay and Gumara watersheds. The climate data was used as input for the General Water Loading Function (GWLF) hydrological model to simulate runoffs.…”

“…GCM-derived atmospheric parameters) and local predictands (e.g. rainfall or temperature) (Abdo et al 2009;Setegn et al 2011;Dile et al 2013;Enyew et al 2014;Ayele et al 2016). However, the climate has a strong association with local soil conditions, topography, and land use dynamics (Haile et al 2009;Collow et al 2014;Cao et al 2015), which is not properly accounted for in a statistical predictor-predictand relationship.…”