Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-543
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
20 Oct 2016
Review status
A revision of this discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Analyzing the future climate change of Upper Blue Nile River Basin (UBNRB) using statistical down scaling techniques
Dagnenet Fenta Mekonnen1,2 and Markus Disse1 1Chair of Hydrology and River Basin Management, Faculty of Civil, Geo and Environmental Engineering, Technische Universität München, Arcisstrasse 21, 80333, Munich, Germany
2Amhara Regional State Water, Irrigation and Energy Development Bureau, Bahirdar, Ethiopia
Abstract. Climate change is becoming one of the most arguable and threatening issues in terms of global context and their responses to environment and socio/economic drivers. Its direct impact becomes critical for water resource development and indirectly for agricultural production, environmental quality, economic development, social well-being. However, a large uncertainty between different Global Circulation Models (GCM) and downscaling methods exist that makes reliable conclusions for a sustainable water management difficult. In order to understand the future climate change of the Upper Blue Nile River Basin, two widely used statistical down scaling techniques namely LARS-WG and SDSM models were applied. Six CMIP3 GCMs for LARS-WG (CSIRO-MK3, ECHAM5-OM, MRI-CGCM2.3.2, HaDCM3, GFDL-CM2.1, CCSM3) model while HadCM3 GCM and canESM2 from CMIP5 GCMs for SDSM were used for climate change analysis.

The downscaled precipitation results from the prediction of the six GCMs by LARS WG showed inconsistency and large inter model variability, two GCMs showed decreasing trend while 4 GCMs showed increasing in the range from −7.9 % to +43.7 % while the ensemble mean of the six GCM result showed increasing trend ranged from 1.0 % to 14.4 %. NCCCS GCM predicted maximum increase in mean annual precipitation. However, the projection from HadCM3 GCM is consistent with the multi-model average projection, which predicts precipitation increase from 1.7 % to 16.6 %. Conversely, the result from all GCMs showed a similar continuous increasing trend for maximum temperature (Tmax) and minimum temperature (Tmin) in all three future periods. The change for mean annual Tmax may increase from 0.4 °c to 4.3 °c whereas the change for mean annual Tmin may increase from 0.3 °c to 4.1 °c.

Meanwhile, the result from SDSM showed an increasing trend for all three climate variables (precipitation, minimum and maximum temperature) from both HadCM3 and canESM2 GCMs. The relative change of mean annual precipitation range from 2.1 % to 43.8 % while the change for mean annual Tmax and Tmin may increase from 0.4 °c to 2.9 °c and from 0.3 °c to 1.6 °c respectively. The change in magnitude for precipitation is higher in RCP8.5 scenarios than others as expected. The present result illustrate that both down scaling techniques have shown comparable and good ability to simulate the current local climate variables which can be adopted for future climate change study with high confidence for the UBNRB. In order to see the comparative downscaling results from the two down scaling techniques, HadCM3 GCM of A2 scenario was used in common. The result obtained from the two down scaling models were found reasonably comparable and both approaches showed increasing trend for precipitation, Tmax and Tmin. However, the analysis of the downscaled climate data from the two techniques showed, LARS WG projected a relatively higher increase than SDSM.


Citation: Mekonnen, D. F. and Disse, M.: Analyzing the future climate change of Upper Blue Nile River Basin (UBNRB) using statistical down scaling techniques, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-543, in review, 2016.
Dagnenet Fenta Mekonnen and Markus Disse
Dagnenet Fenta Mekonnen and Markus Disse
Dagnenet Fenta Mekonnen and Markus Disse

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Short summary
In this study we used multi model GCMs because of recognized inter–variable biases in host GCMs and two widely used statistical down scaling techniques (LARSWG and SDSM) to see comparative performances in Upper Blue Nile River Basin(UBNRB) where there is high climate variability. The result from the two downscaling models suggested that both SDSM and LARS-WG approximate the observed climate data reasonably well and projected an increasing trend for precipitation, maximum and minimum temperature.
In this study we used multi model GCMs because of recognized inter–variable biases in host...
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