Title
Session 4 - Use of numerical groundwater modeling to evaluate uncertainty in conceptual models of recharge and hydrostratigraphy
Location
University of Nevada Las Vegas, Stan Fulton Building
Start Date
1-6-2007 2:09 PM
End Date
1-6-2007 2:16 PM
Description
The uncertainty associated with multiple conceptual models of groundwater recharge and subsurface hydrostratigraphy and the impacts of this uncertainty on predictions made by a regional groundwater flow model is quantitatively evaluated. The composite prediction formally incorporates the uncertainty in the alternative input models using maximum likelihood Bayesian model averaging. The alternative models are weighted by model probability, which is the degree of belief that a model is more plausible given available prior information and site measurements. Flow predictions are found to be more sensitive to hydrostratigraphy than to recharge. Furthermore, posterior model uncertainty is dominated by inter-model variance as opposed to intra-model variance, indicating that conceptual model uncertainty has greater impact on the results than parametric uncertainty. Without consideration of conceptual model uncertainty, uncertainty in the flow predictions would be significantly underestimated.
Keywords
Groundwater flow; Groundwater flow – Model; Groundwater recharge; Hydrology; Hydrostratigraphy; Regional groundwater flow models; Water forecasting; Water-supply – Forecasting
Disciplines
Fresh Water Studies | Hydrology | Natural Resources and Conservation | Water Resource Management
Language
English
Permissions
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COinS
Session 4 - Use of numerical groundwater modeling to evaluate uncertainty in conceptual models of recharge and hydrostratigraphy
University of Nevada Las Vegas, Stan Fulton Building
The uncertainty associated with multiple conceptual models of groundwater recharge and subsurface hydrostratigraphy and the impacts of this uncertainty on predictions made by a regional groundwater flow model is quantitatively evaluated. The composite prediction formally incorporates the uncertainty in the alternative input models using maximum likelihood Bayesian model averaging. The alternative models are weighted by model probability, which is the degree of belief that a model is more plausible given available prior information and site measurements. Flow predictions are found to be more sensitive to hydrostratigraphy than to recharge. Furthermore, posterior model uncertainty is dominated by inter-model variance as opposed to intra-model variance, indicating that conceptual model uncertainty has greater impact on the results than parametric uncertainty. Without consideration of conceptual model uncertainty, uncertainty in the flow predictions would be significantly underestimated.