--> ABSTRACT: Modeling Fluvial Floodplain Deposits, by Overeem, Irina; Syvitski, James ; Kettner, Albert J.; #90142 (2012)
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Modeling Fluvial Floodplain Deposits

Overeem, Irina *1; Syvitski, James 1; Kettner, Albert J.1
(1) INSTAAR-CSDMS, University of Colorado, Boulder, CO.

Floodplain deposition maintains and builds up low-lying lands along rivers and on deltas. Over geological timescales, floodplain deposition and channel migration determine lowland depositional architecture with impacts on reservoir characteristics.

We advanced a 3D floodplain architecture model, AquaTellUs. AquaTellUs uses a nested model approach; a 2D longitudinal profile, embedded as a dynamical flowpath in a 3D grid-based space. We model the main channel belt as a 2D longitudinal profile that responds dynamically to changes in channel geometry, water discharge, sediment load, grain-size distribution and sea level based on first-order, physics-based principles. Sediment flux is described with a modified Exner equation by separate erosion and sedimentation components. Erosion flux along the main flowpath depends on river discharge and channel slope, and is independent of grain-size. Depositional flux along the channel path as well as in the lateral direction into the floodplain depends on the local stream Previous HitvelocityNext Hit, and on grain-dependent settling rates. Lateral depositional patterns are informed by analysis of remote-sensing imagery of recent flood deposits, particularly along the Indus River. Floodplain deposition is an event-driven system — only peak discharge events cause overbank flow, crevasse-splays, and potential channel avulsion. The computational architecture of AquaTellUs preserves stratigraphy by event, allowing for preservation of information of depositional layers of Previous HitvariableTop thickness and composition.

We present stratigraphic sections and pseudo-wells resulting from numerical experiments that show the pronounced effect of different probability density functions for river discharge and sediment load, i.e. flooding recurrence times on the stratigraphic architecture. These experiments generate training images and enhance reservoir geo-statistical modeling.

 

AAPG Search and Discovery Article #90142 © 2012 AAPG Annual Convention and Exhibition, April 22-25, 2012, Long Beach, California