4.7 Article

Apparent directional mass-transfer capacity coefficients in three-dimensional anisotropic heterogeneous aquifers under radial convergent transport

期刊

WATER RESOURCES RESEARCH
卷 50, 期 2, 页码 1205-1224

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1002/2013WR014578

关键词

anisotropy; hydraulic conductivity; connectivity; stratification; mass transfer; anomalous transport

资金

  1. Spanish Ministry of Education through the FPU-Med Scholarship program
  2. Spanish Ministry of Science and Innovation [CSD2009-00065, CGL2012 38120]
  3. National Science Foundation [EAR-1113704]
  4. Directorate For Geosciences
  5. Division Of Earth Sciences [1113704] Funding Source: National Science Foundation

向作者/读者索取更多资源

Aquifer hydraulic properties such as hydraulic conductivity (K) are ubiquitously heterogeneous and typically only a statistical characterization can be sought. Additionally, statistical anisotropy at typical characterization scales is the rule. Thus, regardless of the processes governing solute transport at the local (pore) scale, transport becomes non-Fickian. Mass-transfer models provide an efficient tool that reproduces observed anomalous transport; in some cases though, these models lack predictability as model parameters cannot readily be connected to the physical properties of aquifers. In this study, we focus on a multirate mass-transfer model (MRMT), and in particular the apparent capacity coefficient (), which is a strong indicator of the potential of immobile zones to capture moving solute. We aim to find if the choice of an apparent can be phenomenologically related to measures of statistical anisotropy. We analyzed an ensemble of random simulations of three-dimensional log-transformed multi-Gaussian permeability fields with stationary anisotropic correlation under convergent flow conditions. It was found that apparent also displays an anisotropic behavior, physically controlled by the aquifer directional connectivity, which in turn is controlled by the anisotropic correlation model. A high hydraulic connectivity results in large values. These results provide new insights into the practical use of mass-transfer models for predictive purposes.

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