摘要 :
Simulations of moisture flow in heterogeneous soils are often hampered by lack of measurements of soil hydraulic parameters, making it necessary to rely on other sources of information. In this paper, we develop a methodology to i...
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Simulations of moisture flow in heterogeneous soils are often hampered by lack of measurements of soil hydraulic parameters, making it necessary to rely on other sources of information. In this paper, we develop a methodology to integrate data that can be easily obtained (for example, initial moisture content, θ_i bulk density, and soil texture) with data on soil hydraulic properties via cokriging and Artificial Neural Network (ANN)-based pedotransfer functions. The method is applied to generate heterogeneous soil hydraulic parameters at a field injection site in southeastern Washington State. Stratigraphy at the site consists of imperfectly stratified layers with irregular layer boundaries. Cokriging is first used to generate three-dimensional heterogeneous fields of bulk density and soil texture using an extensive data set of field-measured θ_i, which carry signature about site heterogeneity and stratigraphy. Soil texture and bulk density are subsequently input into an ANN-based site-specific pedotransfer function to generate three-dimensional heterogeneous soil hydraulic parameter fields. The stratigraphy at the site is well represented by the estimated pedotransfer variables and soil hydraulic parameters. The parameter estimates are then used to simulate a field injection experiment at the site. A relatively good agreement is obtained between the simulated and observed moisture contents. The spatial distribution pattern of observed moisture content as well as the southeastward moisture movement is captured well in the simulations. In contrast to earlier work using an effective parameter approach (Yeh et al., 2005), we are able to reproduce the observed splitting of the moisture plume in a coarse sand unit that is sandwiched between two fine-textured units. The simple method of combining cokriging and ANN for site characterization provides unbiased prediction of the observed moisture plume and is flexible so that additional measurements of various types can be included as they become available.
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摘要 :
This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty ass...
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This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty assessment of unsaturated flow and tracer transport in the unsaturated zone of Yucca Mountain, USA. The layer-scale heterogeneity is specific to hydrogeologic layers with layerwise properties, while the local-scale heterogeneity refers to the spatial variation of hydraulic properties within a layer. A Monte Carlo method is used to estimate mean, variance, and 5th, and 95th percentiles for the quantities of interest (e.g., matrix saturation and normalized cumulative mass arrival). Model simulations of unsaturated flow are evaluated by comparing the simulated and observed matrix saturations. Local-scale heterogeneity is examined by comparing the results of this study with those of the previous study that only considers layer-scale heterogeneity. We find that local-scale heterogeneity significantly increases predictive uncertainty in the percolation fluxes and tracer plumes, whereas the mean predictions are only slightly affected by the local-scale heterogeneity. The mean travel time of the conservative and reactive tracers to the water table in the early stage increases significantly due to the local-scale heterogeneity, while the influence of local-scale heterogeneity on travel time gradually decreases over time. Layer-scale heterogeneity is more important than local-scale heterogeneity for simulating overall tracer travel time, suggesting that it would be more cost-effective to reduce the layer-scale parameter uncertainty in order to reduce predictive uncertainty in tracer transport.
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