Finsterle and Faybishenko (1999): Inverse Modeling of a Radial Multistep Outflow Experiment for Determining Unsaturated Hydraulic Properties

Inverse Modeling of a Radial Multistep Outflow Experiment for Determining Unsaturated Hydraulic Properties

Stefan Finsterle and Boris Faybishenko

Advances in Water Resources, 22,(5), 431-444, 1999

Lawrence Berkeley National Laboratory, Earth Sciences Division
University of California, Berkeley, CA 94720

Abstract. Modeling flow and solute transport in the unsaturated zone on the basis of the Richards equation requires specifying values for unsaturated hydraulic conductivity and water potential as a function of saturation. The objectives of the paper are to evaluate the design of a transient, radial, multistep outflow experiment, and to determine unsaturated hydraulic parameters using inverse modeling. We conducted numerical simulations, sensitivity analyses, and synthetic data inversions to assess the suitability of the proposed experiment for concurrently estimating the parameters of interest. We calibrated different conceptual models against transient flow and pressure data from a multistep, radial desaturation experiment to obtain estimates of absolute permeability as well as the parameters of the relative permeability and capillary pressure functions. We discuss the differences in the estimated parameter values and illustrate the impact of the underlying model on the estimates. We demonstrate that a small error in absolute permeability, if determined in an independent experiment, leads to biased estimates of unsaturated hydraulic properties. Therefore, we perform a joint inversion of pressure and flow rate data for the simultaneous determination of permeability and retention parameters, and analyze the correlations between these parameters. We conclude that the proposed combination of a radial desaturation experiment and inverse modeling is suitable for simultaneously determining the unsaturated hydraulic properties of a single soil sample, and that the inverse modeling technique provides the opportunity to analyze data from nonstandard experimental designs.