The Georgia General Assembly, like many states, has enacted pre-defined, comprehensive, drought-mitigation apparatus, but they need rainfall outlooks. Global circulation models (GCMs) provide rainfall outlooks, but they are too spatially course for jurisdictional impact assessment. To wed these efforts, spatially averaged, time-smoothed, daily precipitation observations from the National Weather Service cooperative network are fitted to eight points of 700 mbar atmospheric data from the NCEP/NCAR Reanalysis Project for climate downscaling and drought prediction in the Apalachicola-Chattahoochee-Flint (ACF) river basin. The domain is regionalized with a factor analysis to create specialized models. All models complied well with mathematical assumptions, though the residuals were somewhat skewed and flattened. All models had an R-squared > 0.2. The models revealed map points to the south to be especially influential. A leave-one-out cross-validation showed the models to be unbiased with a percent error of < 20%. Atmospheric parameters are estimated for 2008–2011 with GCMs and empirical extrapolations. The transfer function was invoked on both these data sets for drought predictions. All models and data indicate drought especially for 2010 and especially in the south.

Water resource planning and management practices in the southeastern United States may be vulnerable to climate change. This vulnerability has not been quantified, and decision makers, although generally concerned, are unable to appreciate the extent of the possible impact of climate change nor formulate and adopt mitigating management strategies. Thus, this dissertation aims to fulfill this need by generating decision worthy data and information using an integrated climate change assessment framework. To begin this work, we develop a new joint variable spatial downscaling technique for statistically downscaling gridded climatic variables to generate high-resolution, gridded datasets for regional watershed modeling and assessment. The approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. In the second component of the integrated assessment framework, we develop a data-driven, downward hydrological watershed model for transforming the climate variables obtained from the downscaling procedures to hydrological variables. The watershed model includes several water balance elements with nonlinear storage-release functions. The release functions and parameters are data driven and estimated using a recursive identification methodology suitable for multiple, inter-linked modeling components. The model evolves from larger spatial/temporal scales down to smaller spatial/temporal scales with increasing model structure complexity. For ungauged or poorly-gauged watersheds, we developed and applied regionalization hydrologic models based on stepwise regressions to relate the parameters of the hydrological models to observed watershed responses at specific scales. Finally, we present the climate change assessment results for six river basins in the southeastern United States. The historical (baseline) assessment is based on climatic data for the period 1901 through 2009. The future assessment consists of running the assessment models under all IPCC A1B and A2 climate scenarios for the period from 2000 through 2099. The climate assessment includes temperature, precipitation, and potential evapotranspiration; the hydrology assessment includes primary hydrologic variables (i.e., soil moisture, evapotranspiration, and runoff) for each watershed.