Chance-constrained optimization model for determining river discharges to control sedimentation

C. C. Carriaga, Larry Mays

Research output: Contribution to journalArticle

Abstract

A nonlinear optimization model is formulated for determining optimal reservoir releases to minimize channel bed aggradation and degradation in downstream rivers. The model is based upon minimizing channel bed elevation changes subject to constraints that define system hydraulics (i.e. continuity and energy), sediment routing and transport equations, operational constraints, and boundary conditions. Sediment transport parameters cannot be evaluated with certainty, so the formulation of the optimization model has been extended to accommodate such uncertainties. This is accomplished by a chance-constrained formulation that considers the uncertainties in the sediment load, mean grain size and both the sediment load and mean grain size. The model is applied to a reservoir-river system consisting of an upstream reservoir which is used to regulate discharges to a downstream river. Input to the system is a reservoir inflow hydrograph. The objective is to determine the optimal reservoir releases to minimize aggradation and degradation in the downstream river. The nonlinear chance-constrained model can be solved using the GAMS-MINOS5 or GRG2 codes. (Authors)

Original languageEnglish (US)
Journal[No source information available]
StatePublished - Jan 1 1992

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Constrained optimization
Sedimentation
Rivers
Discharge (fluid mechanics)
Sediments
Uncertainty
Nonlinear Dynamics
Degradation
Sediment transport
Hydraulics
Boundary conditions

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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AB - A nonlinear optimization model is formulated for determining optimal reservoir releases to minimize channel bed aggradation and degradation in downstream rivers. The model is based upon minimizing channel bed elevation changes subject to constraints that define system hydraulics (i.e. continuity and energy), sediment routing and transport equations, operational constraints, and boundary conditions. Sediment transport parameters cannot be evaluated with certainty, so the formulation of the optimization model has been extended to accommodate such uncertainties. This is accomplished by a chance-constrained formulation that considers the uncertainties in the sediment load, mean grain size and both the sediment load and mean grain size. The model is applied to a reservoir-river system consisting of an upstream reservoir which is used to regulate discharges to a downstream river. Input to the system is a reservoir inflow hydrograph. The objective is to determine the optimal reservoir releases to minimize aggradation and degradation in the downstream river. The nonlinear chance-constrained model can be solved using the GAMS-MINOS5 or GRG2 codes. (Authors)

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