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Hydrologic Characterization of Desert Soils with Varying Degrees of Pedogenesis: 2. Inverse Modeling for Effective Properties

^a U.S. Geological Survey, 345 Middlefield Rd., MS 421, Menlo Park, CA 94025
^b Geosciences Dep., 503 Deike Bldg., Pennsylvania State Univ., University Park, PA 16802
^* Corresponding author (jrnimmo{at}usgs.gov).

Received 3 March 2008.

To understand their relation to pedogenic development, soil hydraulic properties in the Mojave Desert were investigated for three deposit types: (i) recently deposited sediments in an active wash, (ii) a soil of early Holocene age, and (iii) a highly developed soil of late Pleistocene age. Effective parameter values were estimated for a simplified model based on Richards' equation using a flow simulator (VS2D), an inverse algorithm (UCODE_2005), and matric pressure and water content data from three ponded infiltration experiments. The inverse problem framework was designed to account for the effects of subsurface lateral spreading of infiltrated water. Although none of the inverse problems converged on a unique, best-fit parameter set, a minimum standard error of regression was reached for each deposit type. Parameter sets from the numerous inversions that reached the minimum error were used to develop probability distributions for each parameter and deposit type. Electrical resistance imaging obtained for two of the three infiltration experiments was used to independently test flow model performance. Simulations for the active wash and Holocene soil successfully depicted the lateral and vertical fluxes. Simulations of the more pedogenically developed Pleistocene soil did not adequately replicate the observed flow processes, which would require a more complex conceptual model to include smaller scale heterogeneities. The inverse-modeling results, however, indicate that with increasing age, the steep slope of the soil water retention curve shifts toward more negative matric pressures. Assigning effective soil hydraulic properties based on soil age provides a promising framework for future development of regional-scale models of soil moisture dynamics in arid environments for land-management applications.

Abbreviations: ERI, electrical resistance imaging • SER, standard error of regression • TDR, time domain reflectometry

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