Improving Applied Groundwater Model Prediction through Parameter Estimation and Uncertainty Analysis

Monday, April 12, 2010: 3:50 p.m.-5:30 p.m.
Tabor Auditorium (Westin Tabor Center, Denver)
Prediction is the ultimate goal of applied groundwater modeling, and prediction uncertainty is vital to managers. Use of parameter estimation and uncertainty analysis to estimate prediction uncertainty has garnered significant attention in research, large-scale government projects, and the academic realm. This session focuses on integrating parameter estimation and uncertainty analysis into groundwater modeling. This session includes presentations from academia, consulting, research institutes, governmental agencies, and people actively applying parameter estimation and uncertainty analysis to improve groundwater model predictions. Case studies highlighting successful, cost-effective application of uncertainty analysis and parameter estimation to politically, socially, or economically sensitive issues are included.
Moderators:
Eileen P. Poeter and Jason R. House
3:50 p.m.
Simulation of Lake Complexes Using a Probabilistic Hydrologic Model with Super-Resolved Calibration
Ganming Liu, Ohio State University; Franklin W. Schwartz, Ohio State University
4:10 p.m.
Using Calibrated Models to Guide Collection of Field Data for Improved Predictions
Claire R. Tiedeman, USGS; Matthew J. Tonkin, S.S. Papadopulos & Associates Inc.; Mary C. Hill, USGS
4:30 p.m.
Quantifying Predictive Uncertainty for a Mountain-Watershed Model
Mengistu Geza, Colorado School of Mines; Eileen P. Poeter, Colorado School of Mines; John McCray, Colorado School of Mines
4:50 p.m.
Hydraulic Conductivity Assessment within a Regional Ground Water Flow Model
Tullia Bonomi, University of Milano-Bicocca; Paola Canepa, University of Milano-Bicocca; Francesca Del Rosso, University of Milano-Bicocca
See more of: Topical Sessions