Direct Breakthrough Curve Prediction From Statistics of Heterogeneous Conductivity Fields

Scott K. Hansen, Claus P. Haslauer, Olaf A. Cirpka, Velimir V. Vesselinov

Research output: Contribution to journalArticlepeer-review


This paper presents a methodology to predict the shape of solute breakthrough curves in heterogeneous aquifers at early times and/or under high degrees of heterogeneity, both cases in which the classical macrodispersion theory may not be applicable. The methodology relies on the observation that breakthrough curves in heterogeneous media are generally well described by lognormal distributions, and mean breakthrough times can be predicted analytically. The log-variance of solute arrival is thus sufficient to completely specify the breakthrough curves, and this is calibrated as a function of aquifer heterogeneity and dimensionless distance from a source plane by means of Monte Carlo analysis and statistical regression. Using the ensemble of simulated groundwater flow and solute transport realizations employed to calibrate the predictive regression, reliability estimates for the prediction are also developed. Additional theoretical contributions include heuristics for the time until an effective macrodispersion coefficient becomes applicable, and also an expression for its magnitude that applies in highly heterogeneous systems. It is seen that the results here represent a way to derive continuous time random walk transition distributions from physical considerations rather than from empirical field calibration.

Original languageAmerican English
Pages (from-to)271-285
Number of pages15
JournalWater Resources Research
Issue number1
StatePublished - 1 Jan 2018
Externally publishedYes


  • heterogeneity
  • predictive modeling
  • solute transport
  • stochastic hydrogeology
  • upscaling

All Science Journal Classification (ASJC) codes

  • Water Science and Technology


Dive into the research topics of 'Direct Breakthrough Curve Prediction From Statistics of Heterogeneous Conductivity Fields'. Together they form a unique fingerprint.

Cite this