This study conducts uncertainty analysis on future region-scale hydrologic projections under the uncertain climate change projections of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report. The hierarchical Bayesian model averaging (HBMA) method is adopted to segregate and prioritize sources of climate projection uncertainty, obtain ensemble mean of hydrologic projection, and quantify the hydrologic projection uncertainty arising from individual uncertainty sources. This study deals with the choices of greenhouse gas (GHG) concentration trajectories, global climate models (GCMs), and GCM initial conditions as three major uncertainty sources in downscaled precipitation and temperature projections. The method is applied to investigating future hydrologic projections in southwestern Mississippi and southeastern Louisiana. The 133 sets of daily precipitation and temperature projections, derived from four Representative Concentration Pathways (RCPs), 21 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and different number of GCM initial conditions. These projections are downscaled to 12 km resolution using Bias Corrected Constructed Analogues (BCCA), and are used as inputs to the hydrologic model HELP3 to project surface runoff, evapotranspiration and groundwater recharge from 2010 to 2099. The results show that future recharge in southwestern Mississippi and southeastern Louisiana is more sensitive to climate projections and exhibits much higher variability than runoff and evapotranspiration. In general, future recharge is projected to increase in next several decades and has great uncertainty toward the end of the century. Runoff is likely to decrease while evapotranspiration is likely to increase in the next century. The major hydrological projection uncertainty comes from the use of different GCMs. Contribution of uncertain GCM initial conditions to hydrologic projections uncertainty reduces over time as contribution from emission path uncertainty becomes more evident.
Key words: Climate Change, CMIP5, RCP, Hydrologic Projection, Uncertainty, Bayesian Model Averaging, Multi-model