Premium
Use of a Numerical Ground‐Water Flow Model for Hypothesis Testing
Author(s) -
Krabbenhoft David P.,
Anderson Mary P.
Publication year - 1986
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.1986.tb01458.x
Subject(s) - geology , shore , lens (geology) , flow (mathematics) , hydraulic conductivity , geotechnical engineering , submarine pipeline , field (mathematics) , groundwater , hydrology (agriculture) , soil science , mechanics , petroleum engineering , oceanography , physics , mathematics , pure mathematics , soil water
Field studies along the southeastern shore of Trout Lake, Wisconsin, documented the presence of downward hydraulic gradients in a known discharge area as well as an anomalous distribution of seepage to the lakebed which deviates significantly from the generally accepted dogma that ground‐water seepage rates decrease exponentially with distance from shore. A numerical ground‐water model facilitated identification of the hydrologic control, namely the presence of a unit of high hydraulic conductivity, that accounts for the anomalous data, and is important for understanding the dynamics of the flow system. Field data including seepage measurements, visual inspection of lakebed materials and springs, and information obtained during drilling, indicate that a lens of coarse‐grained material intersects the lake. However, the significance of the coarsegrained material was not fully appreciated until a ground‐water model was used to simulate the flow system. The model indicated that the presence of the coarse‐grained lens has a marked effect on the flow pattern in the nearshore area causing downward hydraulic gradients which divert ground water into the lens and cause the occurrence of a localized high‐seepage area offshore where the lens intersects the lake. The numerical model predicts the anomalous seepage distribution noted in the field and the downward hydraulic gradients demonstrating that numerical models are practical tools for interpreting field data and for use in hypothesis testing.