[1] This study is mainly aimed at assessing the adequacy of laboratory-based soil hydraulic characterization carried out on undisturbed cores for reproducing the in situ soil hydraulic behavior. Assuming that the laboratory sample size is a representative elementary volume (REV) for a single horizon of the soil profiles examined, the analysis of the hysteretic features of the soil water retention function is used to explain the differences observed in the constitutive soil water relationships as obtained from either laboratory or in situ experiments. Because the wetting procedures differ in laboratory and in field experiments, it is argued that the difference in the obtained hydraulic characteristics is the result of different hysteretic paths. From the comparison of measured hydraulic conductivities we deduce that field and laboratory retention curves are a part of the same hysteresis loop. In such a context the field hydraulic functions can be derived from the laboratory functions using the maximum water content (i.e., at zero pressure head) and air entry value measured in the field. It is shown that the field parameters identified from the measurements in the early phases of the field test are sufficient to reliably describe field hydrological behavior.

This study was conducted to investigate the capability of bimodal approaches in describing water retention data and predicting hydraulic conductivity of 18 samples from an aggregated soil. In this soil, discontinuity in the shape of the water retention curve was encountered and explained by independent draining of the inter‐ and of intraaggregate pores. A single van Genuchten‐type retention curve was unable to describe the observed transition between the pore systems, especially near saturation. Such behavior occurred both when optimizing (VGopt) and when fixing at the measured value (VGfix) the volumetric water content at saturation, θs Because of the predominant effect of the shape of the retention curve near saturation upon the shape of the whole hydraulic conductivity curve, predictions of hydraulic conductivity often differed from unsaturated conductivity observations by even two orders of magnitude. To the contrary, excellent descriptions of retention data were observed when superposition of two unimodal retention curves was adopted. The first function was either a van Genuchten (VGbim) or a simple one‐parameter formulation introduced by Ross and Smettem (RSbim) for describing macroporosity, while the second was in both cases a van Genuchten formulation. The agreement was especially good for higher water content values, leading to values of the coefficient of determination, R2, very close to unity. The laboratory‐measured unsaturated conductivity values compared more closely when bimodal approaches were used, and the predictions were frequently well within one order of magnitude of the measurements.

In this study, a dual‐permeability approach is discussed for modeling preferential flow in shrinking soils by accounting for shrinking effects on macropore and matrix domain hydraulic properties. Conceptually, the soil is treated as a dual‐permeability bulk porous medium consisting of two dynamic interacting pore domains: (1) the fracture (from shrinkage) pore domain and (2) the aggregate (interparticles plus structural) or matrix pore domain. The model assumes that the swell‐shrink dynamics is represented by the inversely proportional volume changes of the fracture and matrix domains, while the overall porosity of the total soil, and hence the layer thickness, remains constant. This assumption can be justified for soils with dominant horizontal soil deformation in the swelling‐shrinkage process (shrinkage geometry factor,rs> 3). The swell‐shrink dynamics was included in a one‐dimensional dual‐permeability model in which water flow in both domains was described with the Richards' equation. Swell‐shrink dynamics was incorporated in the model partly by changing the coupled domain‐specific hydraulic properties according to the shrinkage characteristics of the matrix and partly by allowing the fractional contribution of the two domains to change with the pressure head. As a first step, the hysteresis in the swell‐shrink dynamics was not included. We also assumed that the aggregate behavior and its hydraulic properties depend only on the average aggregate water content and not on its internal real distribution. The model proved, describing successfully effects of shrinkage on the spatial and temporal evolution of water contents measured in a silty loam soil in the field.

[1] Basic soil properties have long been used to predict unsaturated soil hydraulic properties with pedotransfer function (PTFs). Implementation of such PTFs is usually not feasible for catchment-scale studies because of the experimental effort that would be required. On the other hand, topographical attributes are often readily available. This study therefore examines how well PTFs perform that use both basic soil properties and topographical attributes for a hillslope in Basilicata, Italy. Basic soil properties and hydraulic data were determined on soil samples taken at 50-m intervals along a 5-km hillslope transect. Topographical attributes were determined from a digital elevation model. Spearman coefficients showed that elevation (z) was positively correlated with organic carbon (OC) and silt contents (0.62 and 0.59, respectively) and negatively with bulk density (r b ) and sand fraction (À0.34 and À0.37). Retention parameters were somewhat correlated with topographical attributes z, slope (b), aspect (cosf), and potential solar radiation. Water contents were correlated most strongly with elevation (coefficient between 0.38 and 0.48) and aspect during ''wet'' conditions. Artificial neural networks (ANNs) were developed for 21 different sets of predictors to estimate retention parameters, saturated hydraulic conductivity (K s ), and water contents at capillary heads h = 50 cm and 12 bar (10 3 cm). The prediction of retention parameters could be improved with 10% by including topography (RMSE = 0.0327 cm 3 cm À3 ) using textural fractions, r b , OC, z, and b as predictors. Furthermore, OC became a better predictor when the PTF also used z as predictor. The water content at h = 50 cm could be predicted 26% more accurately (RMSE = 0.0231 cm 3 cm À3 ) using texture, r b , OC, z, b, and potential solar radiation as input. Predictions of ANNs with and without topographical attributes were most accurate in the wet range (0 < h < 250 cm). Semivariograms of the hydraulic parameters and their residuals showed that the ANNs could explain part of the (spatial) variability. The results of this study confirm the utility of topographical attributes such as z, b, cosf, and potential solar radiation as predictors for PTFs when basic soil properties are available. A next step would be the use of topographical attributes when no or limited other predictors are available.

No abstract