State Water Resources Research Institute Program (WRRI)

Details for Project ID 2010LA71B, 2010

Multimodel uncertainty analysis for chance-constrained saltwater intrusion management

Institute: Louisiana
Start Date: 2010-03-01 End Date: 2011-02-28
Total Federal Funds: $15,824 Total Non-Federal Funds: $33,714

Principal Investigators: Frank Tsai

Abstract: The project goal is to study the chance-constrained saltwater intrusion remediation problem in the “1,500-foot” sand aquifer, Louisiana under the consideration of model uncertainty. Model uncertainty contains model structure uncertainty and model parameter uncertainty. Due to lack of hydrogeological data, model structure uncertainty leads to non-unique conceptual ground water models. We often find a number of possible conceptual simulation models that explain the same hydrogeological processes. Conceptual models can be different in terms of physical parameters, boundary conditions, initial conditions, sink and source terms, and model geometry. To cope with model uncertainty in the management problem, the objectives of the proposal are (1) to develop a multimodel approach using the Bayesian model averaging (BMA) method to integrate multiple saltwater intrusion models for prediction and management of saltwater intrusion, and (2) to develop a chance-constrained approach to formulate stochastic remediation designs for risk/reliability analysis. Specifically, the project will consider multiple parameterization methods and variogram models for hydraulic conductivity estimation. The project will also consider the uncertainty in initial and boundary conditions to develop a number of saltwater simulation models. Moreover, the chance-constrained formulation is an important approach to study the overdesign issue is the risk-based probabilistic constraint formulation. The degree of overdesign is proportional to the reliability level of constraints that are not violated. The project will involve the BMA statistics into the chance-constrained formulation for a broader risk analysis. The project will adopt a Bayesian model averaging (BMA) method to address the uncertainty and non-uniqueness issues in saltwater intrusion management model. The BMA will integrate multiple saltwater intrusion models for prediction and management of saltwater intrusion under chance-constrained formulation. The project will implement the proposed methodology to the on-going saltwater intrusion problem in the “1,500-foot” sand aquifer in the Baton Rouge area.