Abstract

Abstract. Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 ∘C and the change for mean annual Tmin may increase from 0.3 to 4.1 ∘C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 ∘C and from 0.3 to 1.6 ∘C respectively.

Highlights

  • The impacts of climate change on the hydrological cycle in general and on water resources in particular are of high significance due to the fact that all natural and socioeconomic systems critically depend on water

  • The LARS WG uses the relative change factors (RCFs) derived from the direct general circulation models (GCMs) output of only those variables which directly correspond to the predictands

  • 21 systematically selected future climate scenarios were produced for each time period, which we might think representative to understand fully and to project plausibly the future climate change in the study area and to retain information about the full variability of GCMs

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Summary

Introduction

The impacts of climate change on the hydrological cycle in general and on water resources in particular are of high significance due to the fact that all natural and socioeconomic systems critically depend on water. The direct impact of climate change can be variation and changing pattern of water resources availability and hydrological extreme events such as floods and droughts, with many indirect effects on agriculture, food and energy production and overall water infrastructure (Ebrahim et al, 2013). Disse: Analyzing the future climate change of Upper Blue Nile River basin competition for water is becoming high from different economic, political and social interests of the riparian countries and when runoff variability of upstream countries can greatly affect the downstream countries (Kim, 2008; Semenov and Barrow, 1997)

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