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Bayesian Hierarchical Model Uncertainty Quantification for Future Hydroclimate Projections In Southern Hills-gulf Region, Usa

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This study demonstrates a hierarchical uncertainty analysis on multi-ensemble hydro-climate projections for the southwestern Mississippi and southeastern Louisiana study area considering emission pathway, global climate model (GCM), and GCM initial condition (IC) as three main sources of uncertainty. A total of 133 sets of 1/8° downscaled daily precipitation and temperature projections under 4 emission pathways, 21 Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs, and various ICs are used as inputs to drive the HELP3 hydrologic model to project surface runoff, evapotranspiration and groundwater recharge from 2010–2099. The hierarchical Bayesian model averaging (HBMA) method is adopted to segregate sources of climate projection uncertainty, obtain ensemble mean of hydrologic projections, and quantify the hydrologic projection uncertainty arising from individual uncertainty sources. Results show that future recharge in southwestern Mississippi and southeastern Louisiana is more sensitive to different GCMs and exhibits much higher variability than runoff and evapotranspiration. In general, future recharge is projected to increase in the next several decades and has increasing uncertainty toward the end of the century. Runoff is likely to decrease, while evapotranspiration is likely to increase. The largest hydro-climate model uncertainty comes from the use of different GCMs, which constitutes approximately 62.5–77.0% of total variance. The contribution of IC uncertainty reduces from 23.2% to 8.4% over time (as the contribution of emission pathway uncertainty increases from 4.3% to 29.4%). The HBMA method provides a theoretical sound foundation for the quantification of relative contributions from different sources of uncertainty in multi-ensemble hydro-climate projections.