Applying Downscaled Global Climate Model Data to a Groundwater Model of the Suwannee River Basin, Florida, USA

The application of Global Climate Model (GCM) output to a hydrologic model allows for comparisons between simulated recent and future conditions and provides insight into the dynamics of hydrology as it may be affected by climate change. A previously developed numerical model of the Suwannee River Basin, Florida, USA, was modified and calibrated to represent transient conditions. A simulation of recent conditions was developed for the 372-month period 1970-2000 and was compared with a simulation of future conditions for a similar-length period 2039-2069, which uses downscaled GCM data. The MODFLOW groundwater-simulation code was used in both of these simulations, and two different MODFLOW boundary condition “packages” (River and Streamflow-Routing Packages) were used to represent interactions between surface-water and groundwater features. The hydrologic fluxes between the atmosphere and landscape for the simulation of future conditions were developed from dynamically downscaled precipitation and evapotranspiration (ET) data generated by the Community Climate System Model (CCSM). The downscaled precipitation data were interpolated for the Suwannee River model grid, and the downscaled ET data were used to develop potential ET and were interpolated to the grid. The future period has higher simulated rainfall (10.8 percent) and ET (4.5 percent) than the recent period. The higher future rainfall causes simulated groundwater levels to rise in areas where they are deep and have little ET in either the recent or future case. However, in areas where groundwater levels were originally near the surface, the greater future ET causes groundwater levels to become lower despite the higher projected rainfall. The general implication is that unsaturated zone depth could be more spatially uniform in the future and vegetation that requires a range of conditions (substantially wetter or drier than average) could be detrimentally affected. This vegetation would include wetland species, especially in areas inland from the coast.

Numerical computation of hurricane effects on historic coastal hydrology in Southern Florida

Introduction:Numerical models are critical for assessing the effects of sea level rise(SLR),hurricanes,and storm surge on vegetation change in the Everglades National Park.The model must be capable of representing short-timescale hydrodynamics,salinity transport,and groundwater interaction.However,there is also a strong need to adapt these numerical models to hindcast past conditions in order to examine long-term effects on the distribution of vegetation that cannot be determined using only the modern record.Methods:Based on parameters developed for a numerical model developed for the recent 1996 to 2004 period,a hindcast model was developed to represent sea level and water management for the period of 1926 to 1932,constrained by the limited hydrology and meteorology data available from the historic past.Realistic hurricanewind and storm surge representations,required for the hindcast model,are based on information synthesized from modern storm data.A series of simulation scenarios with various hurricane representations inserted into both hindcast and recent numerical models were used to assess the utility of the storm representation in the model and compare the two simulations.Results:The comparison of the hindcast and recent models showed differences in the hydrology patterns that are consistent with known differences in water delivery systems and sea level rise.A 30×lower-resolution spatially variable wind grid for the hindcast produced similar results to the original high-resolution full wind grid representation of the recent simulation.Storm effects on hydrologic patterns demonstrated with the simulations show hydrologic processes that could have a long-term effect on vegetation change.Conclusions:The hindcast simulation estimated hydrologic processes for the 1926 to 1932 period.It shows promise as a simulator in long-term ecological studies to test hypotheses based on theoretical or empirical-based studies at larger landscape scales.