Accession Number:

ADA583073

Title:

A Computational Model to Simulate Groundwater Seepage Risk in Support of Geotechnical Investigations of Levee and Dam Projects

Descriptive Note:

Final rept.

Corporate Author:

ENGINEER RESEARCH AND DEVELOPMENT CENTER VICKSBURG MS GEOTECHNICAL AND STRUCTURES LAB

Report Date:

2013-03-01

Pagination or Media Count:

44.0

Abstract:

The amount and distribution of coarse-grained sediment relative to fine-grained sediment within a floodplain influences the floodplains geotechnical properties, including the potential for groundwater seepage. Seepage is a primary driver of levee and dam failure, and understanding it is of paramount concern to water resource engineers and managers. This report documents the results of a computational modeling study that simulated alluvial floodplain construction by using simple geomorphic process-imitating rules. The model aggrades an alluvial floodplain, creating floodplain architecture by differentiating between sediment deposited by channel processes coarse sediment and sediment deposited by overbank flood processes fine sediment. The evolution of two floodplain cross sections of the Trinity River near Dallas, Texas, is simulated under five scenarios. The study area is the site of large levee rehabilitation projects in which accurate characterization of the geologic environment has significant engineering importance. Results of the simulations predict that the average channel deposit dimensions are sensitive to the sedimentation scenario employed and are generally similar to those typically observed in fully meandering rivers. The results suggest that the channel aggradation rate influenced heavily the relative channel avulsion frequency during floodplain construction. Increased avulsion frequency equated to more numerous, yet smaller, channel deposits. Avulsion frequency and floodplain width affected the predicted fraction of the floodplains cross-sectional width with subsurface channel deposits. The model for this study is simple and can be run in multiple iterations to produce probabilistic outputs. Such information can be used to predict the data collection density necessary to characterize the geotechnical properties of a project site.

Subject Categories:

  • Geology, Geochemistry and Mineralogy
  • Hydrology, Limnology and Potamology
  • Civil Engineering

Distribution Statement:

APPROVED FOR PUBLIC RELEASE