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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (9): 1023-1036    DOI: 10.1007/s40333-023-0063-y     CSTR: 32276.14.s40333-023-0063-y
Research article     
Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia
Mitiku A WORKU1,*(), Gudina L FEYISA2, Kassahun T BEKETIE2, Emmanuel GARBOLINO3
1Department of Environment and Climate Change Management, Ethiopian Civil Service University, Addis Ababa 1000, Ethiopia
2Center for Environmental Science, Addis Ababa University, Addis Ababa 1000, Ethiopia
3Climpact Data Science, Nova-Sophia 06904, France
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Abstract  

Climate change caused by past, current, and future greenhouse gas emissions has become a major concern for scientists in the field in many countries and regions of the world. This study modelled future precipitation change by downscaling a set of large-scale climate predictor variables (predictors) from the second generation Canadian Earth System Model (CanESM2) under two Representative Concentration Pathway (RCP) emission scenarios (RCP4.5 and RCP8.5) in the semi-arid Borana lowland, southern Ethiopia. The Statistical DownScaling Model (SDSM) 4.2.9 was employed to downscale and project future precipitation change in the middle (2036-2065; 2050s) and far (2066-2095; 2080s) future at the local scale. Historical precipitation observations from eight meteorological stations stretching from 1981 to 1995 and 1996 to 2005 were used for the model calibration and validation, respectively, and the time period of 1981-2018 was considered and used as the baseline period to analyze future precipitation change. The results revealed that the surface-specific humidity and the geopotential height at 500 hPa were the preferred large-scale predictors. Compared to the middle future (2050s), precipitation showed a much greater increase in the far future (2080s) under both RCP4.5 and RCP8.5 scenarios at all meteorological stations (except Teletele and Dillo stations). At Teltele station, the projected annual precipitation will decrease by 26.53% (2050s) and 39.45% (2080s) under RCP4.5 scenario, and 34.99% (2050s) and 60.62% (2080s) under RCP8.5 scenario. Seasonally, the main rainy period would shift from spring (March to May) to autumn (September to November) at Dehas, Dire, Moyale, and Teltele stations, but for Arero and Yabelo stations, spring would consistently receive more precipitation than autumn. It can be concluded that future precipitation in the semi-arid Borana lowland is predicted to differ under the two climate scenarios (RCP4.5 and RCP8.5), showing an increasing trend at most meteorological stations. This information could be helpful for policymakers to design adaptation plans in water resources management, and we suggest that the government should give more attention to improve early warning systems in drought-prone areas by providing dependable climate forecast information as early as possible.

Key words： future precipitation      climate change      second generation Canadian Earth System Model (CanESM2)      Statistical DownScaling Model (SDSM)      semi-arid Borana lowland      southern Ethiopia     
Received: 14 December 2022      Published: 30 September 2023
Corresponding Authors: * Mitiku A WORKU (E-mail: adismite2011@gmail.com)
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Mitiku A WORKU
Gudina L FEYISA
Kassahun T BEKETIE
Emmanuel GARBOLINO
Cite this article:

Mitiku A WORKU, Gudina L FEYISA, Kassahun T BEKETIE, Emmanuel GARBOLINO. Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia. Journal of Arid Land, 2023, 15(9): 1023-1036.

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http://jal.xjegi.com/10.1007/s40333-023-0063-y     OR     http://jal.xjegi.com/Y2023/V15/I9/1023

Fig. 1 Overview of the semi-arid Borana lowland based on digital elevation model (DEM) data. The DEM data were downloaded from the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Archive-Shuttle Radar Topographic Mission (SRTM) with the spatial resolution of 1 arc-second (https://www.usgs.gov).
Fig. 2 Statistical DownScaling Model (SDSM) structure used in this study. CanESM2, second generation Canadian Earth System Model; NCEP/NCAR, National Center for Environmental Prediction/National Center for Atmospheric Research; RCP, Representative Concentration Pathway. r represents the correlation coefficient, and P represents the statistical significance.
No. Predictor Description No. Predictor Description
1 mslp Mean sea level pressure 14 p5zh Divergence at 500 hPa
2 p1_f Geostrophic air flow velocity at surface 15 p8_f Geostrophic air flow velocity at 850 hPa
3 p1_u Zonal velocity component at surface 16 p8_u Zonal velocity component at 850 hPa
4 p1_v Meridional velocity component at surface 17 p8_v Meridional velocity component at 850 hPa
5 p1_z Vorticity at surface 18 p8_z Vorticity at 850 hPa
6 p1th Wind direction at surface 19 p850 850 hPa geopotential height
7 p1zh Divergence at surface 20 p8th Wind direction at 850 hPa
8 p5_f Geostrophic air flow velocity at 500 hPa 21 p8zh Divergence at 850 hPa
9 p5_u Zonal velocity component at 500 hPa 22 prcp Total precipitation
10 p5_v Meridional velocity component at 500 hPa 23 s500 Specific humidity at 500 hPa
11 p5_z Vorticity at 500 hPa 24 s850 Specific humidity at 850 hPa
12 p500 Geopotential height at 500 hPa 25 shum Surface-specific humidity
13 p5th Wind direction at 500 hPa 26 temp Mean temperature at 2 m
Table 1 List of the large-scale predictors in the NCEP/NCAR reanalysis dataset and CanESM2 GCM
Station Calibration period (1981-1995) Validation period (1996-2005)
R2 RMSE R2 RMSE
Arero 0.543 1.736 0.653 4.287
Dehas 0.739 1.478 0.500 2.573
Dillo 0.538 2.694 0.493 0.853
Dire 0.690 2.844 0.617 3.198
Miyo 0.771 1.361 0.582 3.122
Moyale 0.580 0.049 0.480 4.886
Teltele 0.601 5.415 0.897 0.525
Yabelo 0.668 1.785 0.604 3.871
Table 2 Statistical indices for the calibration and validation of the SDSM
Station Predictand Predictor
mslp p500 p8_f p8_u p850 p8zh shum temp
Arero Precipitation √ √ √
Dehas Precipitation √ √
Dillo Precipitation √ √ √
Dire Precipitation √ √ √
Miyo Precipitation √ √
Moyale Precipitation √ √ √
Teltele Precipitation √ √ √
Yabelo Precipitation √ √ √
Table 3 NCEP/NCAR predictors screened out to downscale precipitation in the SDSM
Fig. 3 Projected monthly precipitation from downscaled CanESM2 GCM under RCP4.5 and RCP8.5 scenarios in the middle future (2050s; RCP4.5_2050s and RCP8.5_2050s) and far future (2080s; RCP4.5_2080s and RCP8.5_2080s) compared to the monthly precipitation observations in the baseline period (1981-2018) at the eight meteorological stations. (a), Arero; (b), Dehas; (c), Dillo; (d), Dire; (e), Miyo; (f), Moyale; (g), Teltele; (h), Yabelo.
Fig. 4 Projected seasonal precipitation from downscaled CanESM2 GCM under RCP4.5 and RCP8.5 scenarios in the middle future (2050s; RCP4.5_2050s and RCP8.5_2050s) and far future (2080s; RCP4.5_2080s and RCP8.5_2080s) compared to the seasonal precipitation observations in the baseline period (1981-2018) at the eight meteorological stations. (a), Arero; (b), Dehas; (c), Dillo; (d), Dire; (e), Miyo; (f), Moyale; (g), Teltele; (h), Yabelo.
Climate scenario Percentage change of projected annual precipitation (%)
Arero Dehas Dillo Dire Miyo Moyale Teltele Yabelo
RCP4.5_2050s 85.38 30.27 -2.26 124.63 13.42 119.76 -26.53 14.36
RCP4.5_2080s 141.94 40.07 0.80 154.65 14.69 153.48 -39.45 20.68
RCP8.5_2050s 94.76 48.68 -1.07 140.41 13.22 150.62 -34.99 2.10
RCP8.5_2080s 145.97 61.18 3.20 200.69 24.64 199.62 -60.62 12.64
Table 4 Percentage change of projected annual precipitation in the middle (2050s) and far (2080s) future compared to the precipitation observations in the baseline period (1981-2018) at the eight meteorological stations
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