期刊
ADVANCES IN WATER RESOURCES
卷 75, 期 -, 页码 53-66出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2014.10.011
关键词
Heterogeneous; Geostatistics; High resolution; Uncertainty; Spatial moment; Breakthrough curve
资金
- DOE NETL [DE-FE0002059]
- EPA STAR [RD-83438701-0]
- Golden Energy Computing Organization at the Colorado School of Mines
- National Science Foundation
- National Renewable Energy Laboratory
- US Environmental Protection Agency's Science to Achieve Results (STAR) program
- US Environmental Protection Agency's STAR program [RD-83438701-0]
Aquifer heterogeneity is known to affect solute characteristics such as spatial spreading, mixing, and residence time, and is often modeled geostatistically to address aquifer uncertainties. While parameter uncertainty is often considered, the model uncertainty of the heterogeneity structure is frequently ignored. In this high-resolution heterogeneity model comparison, we perform a stochastic analysis utilizing spatial moment and breakthrough curve (BTC) metrics on Gaussian (G), truncated Gaussian (TG), and non-Gaussian, or facies'' (F) heterogeneous domains. Three-dimensional plume behavior is rigorously assessed with meter (horizontal) and cm (vertical) scale discretization over a ten-kilometer aquifer. Model differences are quantified as a function of statistical anisotropy, epsilon, by varying the x-direction integral scale of hydraulic conductivity, K, from 15 to 960 (m). We demonstrate that the model is important only for certain metrics within a range of epsilon. For example, spreading is insensitive to the model selection at low epsilon, but not at high epsilon. In contrast, center of mass is sensitive to the model selection at low epsilon, and not at high epsilon. A conceptual model to explain these trends is proposed and validated with BTC metrics. Simulations show that G model effective K, and 1st and 2nd spatial moments are much greater than that of TG and F models. A comparison of G and TG models (which only differ in K-distribution tails) reveal drastically different behavior, exemplifying how accurate characterization of the K-distribution may be important in modeling efforts, especially in aquifers where extreme K values are often not measured, or inadvertently overlooked. (C) 2014 Elsevier Ltd. All rights reserved.
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