2006 Philadelphia Annual Meeting (22–25 October 2006)

Paper No. 7
Presentation Time: 3:00 PM


KONIKOW, Leonard F., U.S. Geol Survey, 431 National Center, Reston, VA 20192, lkonikow@usgs.gov

The collection and analysis of hydrogeologic data provide the basis for formulating a conceptual model that describes how a ground-water system operates, and calibrating a deterministic numerical model to predict how the system will change in the future. Some ground-water data represent values of model parameters, such as hydraulic conductivity, but uncertainty in the extrapolation, interpolation, zonation, continuity, and variability of these parameter values creates the need to calibrate ground-water models and estimate a continuous distribution of parameter values. Additional data, representing observations of the state of the system (the dependent variables in the model, such as hydraulic head and ground-water discharges), are needed to develop a best fit between the model and the real system. An efficient modeling strategy is to cycle from the data and conceptual model to the numerical model and back again—reexamining all prior assumptions during the feedback process. If the data analysis and the conceptual model (including ground-water processes, geologic framework, and boundary conditions) evolve during each iteration, the numerical model will consequently improve, as illustrated by examples from development of a regional model of the Madison Group aquifer system in the Northern Great Plains and a local-scale model of the Rocky Mountain Arsenal area near Denver, Colorado. The process of data compilation and analysis for model development has benefited from recent advances in parameter-estimation modeling, GIS capabilities, geologic modeling, ground-water age dating, Graphical User Interfaces, and use of the Internet to improve data accessibility. However, relating data collected at one scale of measurement to equivalent values in a model developed for a different scale of analysis remains problematic.