Statistical Downscaling of Daily Temperature and Rainfall Data from Global Circulation Models, in Borkena River Catchment, Ethiopia

摘要

Statistical downscaling models for temperature and precipitation in Borkena River Catchment, have been developed and applied to calculate the changes in historic, current and future climate changes. Projected changes in precipitation and temperature were analyzed using the outputs from GCMs and stations data (1976-2005) which were collected from 4 observed meteorological stations (predictand) and downscaling using SDSM version 4.2.9a. The study investigated how these changes in temperature and precipitation might translate into changes in livelihoods of people and other biophysical components using impact assessments. The objective of this paper was to evaluate the statistical downscaling methods in estimating monthly average rainfall and temperature, in line with this it was also conducted to project the future climate situation of Borkena River catchment (Ethiopia). Accordingly, the results revealed that both temperatures (maximum and minimum) showed an increasing trend. The result of future temperature from SDSM for RCP4.5 and RCP8.5 show increased rate on annual basis except 202s for RCP4.5. The maximum increasing value will occur for RCP8.5 in the 2020s and 2080s with a value of 1.85 and 2.82 for minimum and maximum temperature respectively. Downscaled precipitation results of future time interval of this study indicated decreased precipitation value for both RCP4.5 and RCP8.5 on annual basis. This, may increase water availability stresses in the study area. Therefore, this facilitate the decision makers to incorporate climate change scenarios for devising sustainable strategies, including: water harvesting technologies, supplementary irrigation, using improved seeds, which can tolerate moisture stresses, afforestation and reforestation programs, and soil and water conservation techniques. Moreover, crop diversifications, agricultural extension services access, related strategies, and measures are highly recommended for climate change resiliencies.