HR: 0800h
AN: H21C-0702    [Abstracts]
TI: Soil and vegetation parameter estimation using chemical and physical unsaturated zone data
AU: * Ng, G C
EM: gng@mit.edu
AF: Massachusetts Institute of Technology, Parsons Laboratory Bldg. 48 15 Vassar St., Cambridge, MA 02139, United States
AU: McLaughlin, D
EM: dennism@mit.edu
AF: Massachusetts Institute of Technology, Parsons Laboratory Bldg. 48 15 Vassar St., Cambridge, MA 02139, United States
AU: Entekhabi, D
EM: darae@mit.edu
AF: Massachusetts Institute of Technology, Parsons Laboratory Bldg. 48 15 Vassar St., Cambridge, MA 02139, United States
AU: Scanlon, B
EM: bridget.scanlon@beg.utexas.edu
AF: University of Texas at Austin, Bureau of Economic Geology Jackson School of Geosciences J.J. Pickle Research Campus, Bldg. 130 10100 Burnet Rd., Austin, TX 78758-4445, United States
AB: Subsurface fluxes can be very sensitive to environmental changes. In particular, the agricultural clearing of native vegetation in semi-arid regions has allowed significant direct groundwater recharge to occur where only negligible amounts of moisture percolated past the root zone before. In order to predict how this small, yet important flux may respond to future adaptations, it is essential to quantify how meteorological forcing, soil conditions, and root extraction interact to effect recharge. Physically-based numerical models provide a means to test the various controls on recharge, yet realistic parameters are needed. We explore the use of physical and chemical unsaturated zone data to estimate the soil and vegetation parameters used in subsurface flux simulations. Soil and vegetation parameters are estimated using an augmented state ensemble approach. The method allows for uncertainty quantification of the estimated parameters. This parameter estimation work is useful because it can reveal the ties between existing recharge conditions and the physical features underlying the parameters. Furthermore, probabilistic ensemble predictions can be made for future recharge scenarios. The feasibility of this parameter estimation approach is first demonstrated using synthetic tests; it is then applied to the Southern High Plains of Texas, where replacement of native grasslands with dryland cotton crops has yielded increased recharge over the past century.
DE: 1813 Eco-hydrology
DE: 1816 Estimation and forecasting
DE: 1846 Model calibration (3333)
DE: 1873 Uncertainty assessment (3275)
DE: 1875 Vadose zone
SC: Hydrology [H]
MN: 2007 Fall Meeting