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Natural Stimuli Calibration with Fining Direction Regularization in an Integrated Hydrologic Model.

Steven J Berg1, Nancy R Grosso2, Michael P Sherrier2

  • 1Department of Earth and Environmental Sciences, University of Waterloo, 200 University Ave W. Waterloo, Ontario, N2L 3G1, Canada.

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Summary
This summary is machine-generated.

This study calibrates a HydroGeoSphere model for river flood events, improving surface and groundwater interaction simulations. The new method incorporates depositional history for more realistic hydraulic conductivity fields.

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Area of Science:

  • Environmental science
  • Hydrogeology
  • Geoscience

Background:

  • Traditional models of surface water-groundwater interaction during floods often oversimplify complex riverine systems.
  • Ignoring bank soil heterogeneity and simplifying river geometry limits the applicability of existing models.

Purpose of the Study:

  • To develop and calibrate a site-specific, fully-integrated surface and subsurface HydroGeoSphere model.
  • To incorporate depositional history and soil heterogeneity into flood event modeling for improved accuracy.

Main Methods:

  • Calibration of a HydroGeoSphere model using flood events from the South River, Waynesboro, VA.
  • Utilized fining direction regularization with a highly parameterized inversion driven by natural stimuli.
  • Completed over 25,000 simulations using parallel computing for model calibration.

Main Results:

  • Generated multiple realistic hydraulic conductivity fields reflecting the riverbank's depositional history.
  • Successfully calibrated a complex model with 365 material zones to dense well network data during flood events.
  • Demonstrated the effectiveness of fining direction regularization in accounting for depositional environments.

Conclusions:

  • Site-specific HydroGeoSphere modeling enhanced with depositional history provides more accurate simulations of surface water-groundwater interactions.
  • The calibration approach offers a pathway to develop realistic subsurface models that honor site-specific geological context.
  • This methodology improves the understanding and prediction of hydrological processes during riverine flood events.