Analyzing Uncertainty Drivers of Climate Change Impact Studies in Tropical and Arid Climates

Uncertainty in climate change impact studies can be identified in general circulation models (GCM), emission scenarios, downscaling techniques, hydrological models and data. Despite being aleatory or epistemic -sourced uncertainties, their effect size on impact model output can be variable in different climate zones. To test this hypothesis, the present study explored the uncertainty level of each source in the projected streamflow scenarios of the Sarbaz river basin (SRB) with arid climate properties and the tropical Hulu Langat river basin (HLB). An ensemble of five GCMs bias-corrected using EquiDistant-CDF-matching method, two representative concentration pathways (RCPs), and hydrological model parameters sets sourced the overall uncertainty in each case study. Investigations were performed at mean monthly scale of three 30-year periods of baseline, 2016-2045 (2030s), and 2046-2075 (2060s). In both climates, on average, GCM uncertainty was the largest contributor at monthly scale analysis and its effect size escalated following the monsoon months. In the tropical HLB, GCM uncertainty increased across the periods, but it did not show a similar pattern in the arid region of SRB. The RCP uncertainty showed the least effect size during the baseline period and it peaked in 2030s in the HLB climate. However, the only pattern recognizable in the arid SRB was the intensified effect size of all uncertainty sources along with the tremendous impacts projected in monsoon months; at least twice the size of uncertainty sources effect and impacts projected for the tropical HLB. The uncertainty sources effect size altered dramatically as the climate-of the study area-changed. Thus, this research emboldens the need for seasonal-based analysis of uncertainty sources in climate change impact studies at dry climate zones.