HR: 1330h
AN: H32C-0583    [PDF]
TI: Evaluating Climate, Vegetation, and Soil Controls on Groundwater Recharge Using Unsaturated Flow Modeling
AU: * Keese, K E
EM: kelley.keese@beg.utexas.edu
AF: Univ. of Texas at Austin, Jackson School of Geosciences, Pickle Research Campus, 10100 Burnet Rd., Austin, TX 78758 United States
AU: Scanlon, B R
EM: bridget.scanlon@beg.utexas.edu
AF: Univ. of Texas at Austin, Jackson School of Geosciences, Pickle Research Campus, 10100 Burnet Rd., Austin, TX 78758 United States
AU: Reedy, R C
EM: bob.reedy@beg.utexas.edu
AF: Univ. of Texas at Austin, Jackson School of Geosciences, Pickle Research Campus, 10100 Burnet Rd., Austin, TX 78758 United States
AB: Understanding the relative importance of climate, vegetation, and soils in controlling groundwater recharge is critical for estimating recharge rates and for assessing the importance of these factors in controlling aquifer vulnerability to contamination. Understanding the role of climate and vegetation in controlling recharge will also be valuable in determining impacts of climate change and land use change on recharge. Numerical modeling is a valuable tool for assessing controls on recharge and for developing a predictive understanding of recharge processes. Unsaturated flow modeling was used to simulate recharge for a range of climate (arid - humid), vegetation (shrub, grass, forest, crops), and soils (fine - coarse grained, monolithic - layered) using data from Texas. Data from 10 meteorological stations in the state provided long-term (30 yr) climate forcing ranging from arid to humid conditions. Spatial distribution of dominant vegetation associations provided by the USGS was used to assign vegetation parameters using GIS and including fractional vegetation coverage, leaf area index, root depth, and root length density. Varying levels of soils data were used in the simulations ranging from simple monolithic sand profiles to complex layered soil profiles from the SSURGO database and pedotransfer functions were used to translate soils data to hydraulic parameters for the simulations. The effect of climate was evaluated using monolithic sand profiles without vegetation. Recharge rates varied from 54 mm/yr in arid west Texas to 720 mm/yr in humid east Texas, correlating positively with precipitation (R=0.99, slope = 0.69). These recharge rates represent 24 to 61 percent of long-term average precipitation. High potential recharge rates in monolithic sand profiles indicate that climate is not the limiting factor controlling recharge and that vegetation and soil texture are important in reducing recharge. Addition of vegetation to the monolithic sand profiles reduced recharge rates for most cases by factors ranging from 2 to 11. Soil profile layering reduced recharge rates in most cases relative to recharge rates based on monolithic sand profiles by factors ranging from 2 to 10. Recharge estimates based on nonvegetated, layered soil profiles were quite variable locally depending on soil texture and sequence of layers. However, aerially weighted average recharge rates for the counties analyzed in this study were much less variable and were positively correlated with precipitation (R=0.79; slope = 0.19). The final simulations included vegetation and layered soil profiles and resulted in recharge rates ranging from 0 to 328 mm/yr which represent reductions from potential recharge by factors ranging from 6 to 380. Unsaturated flow modeling proved to be a useful tool in evaluating the effects of climate, soil, and vegetation on recharge because these factors could be isolated in different simulations. Modeling results indicate that long-term average precipitation can be used as a predictor of recharge, but is not the limiting factor; vegetation and soil texture are important in reducing recharge. The results of this study have important implications for estimating recharge and indicate that the role of vegetation and soil texture in reducing recharge could significantly impact aquifer vulnerability to contamination.
DE: 1836 Hydrologic budget (1655)
DE: 3337 Numerical modeling and data assimilation
SC: Hydrology [H]
MN: 2003 Fall Meeting