# Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty

## Abstract

We describe the development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling that considers the combined impact of hydrogeologic uncertainties associated with the conceptual-mathematical basis of a model, model parameters, and the scenario to which the model is applied. The methodology is based on an extension of a Maximum Likelihood implementation of Bayesian Model Averaging. Model uncertainty is represented by postulating a discrete set of alternative conceptual models for a site with associated prior model probabilities that reflect a belief about the relative plausibility of each model based on its apparent consistency with available knowledge and data. Posterior model probabilities are computed and parameter uncertainty is estimated by calibrating each model to observed system behavior; prior parameter estimates are optionally included. Scenario uncertainty is represented as a discrete set of alternative future conditions affecting boundary conditions, source/sink terms, or other aspects of the models, with associated prior scenario probabilities. A joint assessment of uncertainty results from combining model predictions computed under each scenario using as weights the posterior model and prior scenario probabilities.

- Authors:

- Publication Date:

- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 947942

- Report Number(s):
- PNNL-SA-47117

401001060; TRN: US200905%%239

- DOE Contract Number:
- AC05-76RL01830

- Resource Type:
- Conference

- Resource Relation:
- Conference: Proceedings of the 3rd Federal Interagency Hydrologic Modeling Conference

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 58 GEOSCIENCES; HYDROLOGY; GEOLOGIC MODELS; GROUND WATER; FLOW MODELS; DATA COVARIANCES; BOUNDARY CONDITIONS; ENVIRONMENTAL TRANSPORT; modeling; hydrogeologic; uncertainty; parameter uncertainty; model uncertainty; scenario

### Citation Formats

```
Meyer, Philip D., Ye, Ming, Neuman, Shlomo P., Rockhold, Mark L., Cantrell, Kirk J., and Nicholson, Thomas J.
```*Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty*. United States: N. p., 2006.
Web.

```
Meyer, Philip D., Ye, Ming, Neuman, Shlomo P., Rockhold, Mark L., Cantrell, Kirk J., & Nicholson, Thomas J.
```*Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty*. United States.

```
Meyer, Philip D., Ye, Ming, Neuman, Shlomo P., Rockhold, Mark L., Cantrell, Kirk J., and Nicholson, Thomas J. Mon .
"Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty". United States.
doi:.
```

```
@article{osti_947942,
```

title = {Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty},

author = {Meyer, Philip D. and Ye, Ming and Neuman, Shlomo P. and Rockhold, Mark L. and Cantrell, Kirk J. and Nicholson, Thomas J.},

abstractNote = {We describe the development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling that considers the combined impact of hydrogeologic uncertainties associated with the conceptual-mathematical basis of a model, model parameters, and the scenario to which the model is applied. The methodology is based on an extension of a Maximum Likelihood implementation of Bayesian Model Averaging. Model uncertainty is represented by postulating a discrete set of alternative conceptual models for a site with associated prior model probabilities that reflect a belief about the relative plausibility of each model based on its apparent consistency with available knowledge and data. Posterior model probabilities are computed and parameter uncertainty is estimated by calibrating each model to observed system behavior; prior parameter estimates are optionally included. Scenario uncertainty is represented as a discrete set of alternative future conditions affecting boundary conditions, source/sink terms, or other aspects of the models, with associated prior scenario probabilities. A joint assessment of uncertainty results from combining model predictions computed under each scenario using as weights the posterior model and prior scenario probabilities.},

doi = {},

journal = {},

number = ,

volume = ,

place = {United States},

year = {Mon May 01 00:00:00 EDT 2006},

month = {Mon May 01 00:00:00 EDT 2006}

}