Determination of hydraulic conductivity from grain-size distribution for different depositional environments

Jorge Rosas, Oliver Miguel Lopez Valencia, Thomas M. Missimer, Kapo M. Coulibaly, Abdullah Dehwah, Kathryn Sesler, Luis R. Lujan Rodri­guez, David Mantilla Calderon

Research output: Contribution to journalArticlepeer-review

106 Scopus citations


Over 400 unlithified sediment samples were collected from four different depositional environments in global locations and the grain-size distribution, porosity, and hydraulic conductivity were measured using standard methods. The measured hydraulic conductivity values were then compared to values calculated using 20 different empirical equations (e.g., Hazen, Carman-Kozeny) commonly used to estimate hydraulic conductivity from grain-size distribution. It was found that most of the hydraulic conductivity values estimated from the empirical equations correlated very poorly to the measured hydraulic conductivity values with errors ranging to over 500%. To improve the empirical estimation methodology, the samples were grouped by depositional environment and subdivided into subgroups based on lithology and mud percentage. The empirical methods were then analyzed to assess which methods best estimated the measured values. Modifications of the empirical equations, including changes to special coefficients and addition of offsets, were made to produce modified equations that considerably improve the hydraulic conductivity estimates from grain size data for beach, dune, offshore marine, and river sediments. Estimated hydraulic conductivity errors were reduced to 6 to 7.1m/day for the beach subgroups, 3.4 to 7.1m/day for dune subgroups, and 2.2 to 11m/day for offshore sediments subgroups. Improvements were made for river environments, but still produced high errors between 13 and 23m/day. © 2013, National Ground Water Association.
Original languageEnglish (US)
Pages (from-to)399-413
Number of pages15
Issue number3
StatePublished - Jun 6 2013

ASJC Scopus subject areas

  • Water Science and Technology
  • Computers in Earth Sciences


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