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Climate Change Downscaling Using Stochastic Weather Generator Model in Rift Valley Basins of Ethiopia

Climate Change Downscaling Using Stochastic Weather Generator Model in Rift Valley Basins of Ethiopia
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摘要 Agriculture is the mainstay of Ethiopian economy. Developing country like Ethiopia suffers from climate change, due to their limited economic capability to build irrigation projects to combat the trouble. This study generates climate change in rift valley basins of Ethiopia for three time periods (2020s, 2055s and 2090s) by using two emission scenarios: SRA1B and SRB1 for faster technological and environmental extreme respectively. First, outputs of 15 General Circulation Models (GCMs) under two emission scenarios (SRA1B and SRB1) are statistically downscaled by using LARS-WG software. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. The study result indicates that LARS-WG 5.5 version model is more uncertain to simulate future mean rainfall than generating maximum and minimum mean temperatures. GCMs weight difference for mean rainfall is 0.83 whereas weight difference for minimum and maximum mean temperatures is 0.09 among GCMs models. The study results indicate minimum and maximum temperatures absolute increase in the range of 0.34˚C to 0.58˚C, 0.94˚C to 1.8˚C and 1.42˚C to 3.2˚C and 0.32˚C to 0.56˚C, 0.91˚C to 1.8˚C and 1.34˚C to 3.04˚C respectively in the near-term (2020s), mid-term (2055s) and long-term (2090s) under both emission scenarios. The expected rainfall change percentage during these three time periods considering this GCMs weight difference into account ranges from -2.3% to 7%, 0.375% to 15.83% and 2.625% to 31.1% in the same three time periods. In conclusion, the study results indicate that in coming three time periods, maximum and minimum temperature and rainfall increase is expected in rift valley of basins of Ethiopia. Agriculture is the mainstay of Ethiopian economy. Developing country like Ethiopia suffers from climate change, due to their limited economic capability to build irrigation projects to combat the trouble. This study generates climate change in rift valley basins of Ethiopia for three time periods (2020s, 2055s and 2090s) by using two emission scenarios: SRA1B and SRB1 for faster technological and environmental extreme respectively. First, outputs of 15 General Circulation Models (GCMs) under two emission scenarios (SRA1B and SRB1) are statistically downscaled by using LARS-WG software. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. The study result indicates that LARS-WG 5.5 version model is more uncertain to simulate future mean rainfall than generating maximum and minimum mean temperatures. GCMs weight difference for mean rainfall is 0.83 whereas weight difference for minimum and maximum mean temperatures is 0.09 among GCMs models. The study results indicate minimum and maximum temperatures absolute increase in the range of 0.34˚C to 0.58˚C, 0.94˚C to 1.8˚C and 1.42˚C to 3.2˚C and 0.32˚C to 0.56˚C, 0.91˚C to 1.8˚C and 1.34˚C to 3.04˚C respectively in the near-term (2020s), mid-term (2055s) and long-term (2090s) under both emission scenarios. The expected rainfall change percentage during these three time periods considering this GCMs weight difference into account ranges from -2.3% to 7%, 0.375% to 15.83% and 2.625% to 31.1% in the same three time periods. In conclusion, the study results indicate that in coming three time periods, maximum and minimum temperature and rainfall increase is expected in rift valley of basins of Ethiopia.
出处 《American Journal of Climate Change》 2019年第4期561-590,共30页 美国气候变化期刊(英文)
关键词 Climate Change IPCC AR4 LARS-WG Statistical DOWNSCALING 15 GCM ENSEMBLES Climate Change IPCC AR4 LARS-WG Statistical Downscaling 15 GCM Ensembles
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