Projections and impact assessment of the local climate change conditions of the Black Volta Basin of Ghana based on the Statistical DownScaling Model

Abstract

The uncertainties and biases associated with Global Climate Models (GCMs) ascend from global to regional and local scales which delimits the applicability and suitability of GCMs in site-specific impact assessment research. The study downscaled two GCMs to evaluate effects of climate change (CC) in the Black Volta Basin (BVB) using Statistical DownScaling Model (SDSM) and 40-year ground station data. The study employed Taylor diagrams, dimensionless, dimensioned, and goodness of fit statistics to evaluate model performance. SDSM produced good performance in downscaling daily precipitation, maximum and minimum temperature in the basin. Future projections of precipitation by HadCM3 and CanESM2 indicated decreasing trend as revealed by the delta statistics and ITA plots. Both models projected near- to far-future increases in temperature and decreases in precipitation by 2.05-23.89, 5.41–46.35, and 5.84–35.33% in the near, mid, and far future respectively. Therefore, BVB is expected to become hotter and drier by 2100. As such, climate actions to combat detrimental effects on the BVB must be revamped since the basin hosts one of the largest hydropower dams in Ghana. The study is expected to support the integration of CC mitigation into local, national, and international policies, and support knowledge and capacity building to meet CC challenges. HIGHLIGHTS The study downscaled two GCMs to evaluate the effects of climate change over the Black Volta Basin using the SDSM.; The study revealed that the SDSM provided a good performance across the selected stations.; Both models projected near-to-future decreases in precipitation in the near-, mid-, and far-future.; The Black Volta Basin is expected to become hotter and drier by 2100.; The SDSM is a powerful tool for climate change assessment.;

Keywords

• black volta basin
• climate change
• gcms
• impact assessment
• innovative-sen trend analysis
• sdsm