Modeling of spatial and vertical variability in soil hydraulic properties is a pervasive dilemma in computational hydrology. In an effort to provide guidance in inverse modeling of soil hydraulic properties, this study (i) calibrated soil hydraulic properties, for different soil layer depths, to measured soil moisture, (ii) identified the best-suited soil thickness through validation, and (iii) post-validated the resulting best-fit soil properties and layer depth to the observed soil stratigraphic conditions. Soil property depths were varied to identify the most important stratigraphic features in soil water modeling. Statistical and graphic goodness-of-fit measures indicated that calibration and validation simulations performed better at shallow depths and generally worsened with depth. A comparison of differences in modeled soil layering at two field sites reflected differences in the observed soil stratigraphic conditions. Comparison with in situ soil stratigraphy indicated the importance of soil horizon changes and human-induced alterations to the near-surface soils. These results indicate that soil moisture dynamics can be effectively modeled using basic soil input data and inverse methods. However, to improve model performance, a site-specific field monitoring campaign is needed to properly account for the effects of soil stratigraphy and boundary conditions.

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