Abstract
The study investigates the hierarchical uncertainty of multi-ensemble hydroclimate projections for the Southern Hills-Gulf region, USA, considering emission pathways and a global climate model (GCM) as two main sources of uncertainty. Forty projections of downscaled daily air temperature and precipitation from 2010 to 2099 under four emission pathways and ten CMIP5 GCMs are adopted for hydroclimate modeling via the HELP3 hydrologic model. This study focuses on evapotranspiration (ET), surface runoff, and groundwater recharge projections in this century. Climate projection uncertainty is characterized by the hierarchical Bayesian model averaging (HBMA) method, which segregates emission pathway uncertainty and climate model uncertainty. HBMA is able to derive ensemble means and standard deviations, arising from individual uncertainty sources, for ET, runoff, and recharge. The model results show that future recharge in the Southern Hills-Gulf region is more sensitive to different climate projections and exhibits higher variability than ET and runoff. Overall, ET is likely to increase and runoff is likely to decrease in this century given the current emission path scenarios. Runoff are predicted to have an 18% to 20% decrease and ET is predicted to have around a 3% increase throughout the century. Groundwater recharge is likely to increase in this century with a decreasing trend. Recharge would increase about 13% in the early century and will have only a 3% increase in the late century. All hydrological projections have increasing uncertainty towards the end of the century. The HBMA result suggests that the GCM uncertainty dominates the overall hydrological projection uncertainty in the early century and the mid-century. The emission pathway uncertainty becomes important in the late century.
Highlights
Uncertainties in hydroclimate projections provide necessary information to support the planning and management of future water recourses, but those uncertainties have not been sufficiently addressed in the IPCC (Intergovernmental Panel on Climate Change) Fifth Assessment [1]
Based on Equations (13) and (14), we evaluated the posterior model probabilities for the 10 global climate model (GCM) listed in Table 1 using simulated and observed 1950–2006 monthly temperature and precipitation observations
Mean and one standard deviation interval resulted from the 10 GCMs and 4 emission pathways in a hierarchical order (Figure 3)
Summary
Uncertainties in hydroclimate projections provide necessary information to support the planning and management of future water recourses, but those uncertainties have not been sufficiently addressed in the IPCC (Intergovernmental Panel on Climate Change) Fifth Assessment [1]. To integrate projections and quantify uncertainty from using multiple prediction models, Bayesian model averaging (BMA) [27] takes into account observational data evidence to determine model weights for averaging, and it performs better than other multi-model methods [28,29] Many studies, such as weather forecasting [30,31], ensemble of climate models [32,33,34,35], and hydrological prediction [36,37,38,39,40,41,42], have adopted BMA for ensemble prediction and uncertainty analysis. Hydrologic modeling; Section 4 discusses the results and findings; and Section 5 concludes the study
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