|Institution:||Louisiana State University|
|Department:||Civil & Environmental Engineering|
|Keywords:||Hydrologic modeling; Water budget; Groundwater recharge; Multi-model; Bayesian Model Averaging; CMIP5|
|Full text PDF:||http://etd.lsu.edu/docs/available/etd-03312015-144718/|
This study conducts uncertainty analysis on future region-scale hydrologic projections under the uncertain climate change projections of the IPCC Fifth and Forth Assessment Reports. 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 investigate future hydrologic projections in southwestern Mississippi and southeastern Louisiana. Six sets and 133 sets of 1/8-degree-BCCA-downscaled daily precipitation and temperature projections, respectively, from CMIP3 and CMIP5 climate projections, are used as inputs to the hydrologic model HELP3 to project surface runoff, evapotranspiration and groundwater recharge from 2010 to 2099. The 6 sets derived from B1, A2 and A1FI emission scenarios of the PCM and GFDL models and the 133 sets derived from four emission paths, 21 CMIP5 GCMs, and different number of GCM initial conditions. 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.