Date of Completion


Embargo Period



Zoi Dokou, Jeffrey Starn

Field of Study

Environmental Engineering


Master of Science

Open Access

Open Access


This study is an effort to investigate two parameter estimation approaches in groundwater modeling, sequential and combined. Most researches imply that simultaneous use of flow and transport observations would be more beneficial in parameter estimation, however some other researchers rise questions about this approach. They believe due to the differing geological properties of aquifers and consequently differing mathematical basis of groundwater flow compared to transport simultaneous use of different observations might not be useful in every cases. Despite the fact that parameter estimation or inverse modeling is not a new method in groundwater modeling, most modelers tend to use forward modeling to estimate parameters. In this research a synthetic heterogeneous K-field is created using SGeMS Sequential Gaussian Simulation. This K-field, derived zones of porosity and defined boundary conditions make our simulated confined aquifer. The model synthetic observations obtained from forward models MODFLOW and MODPATH, are used in the process of parameter estimation using PEST++. In order to have a better look at the model and find its weak points, different scenarios have been defined. Applying the principle of parsimony, difficulties have been added gradually in each scenario for the model. The sequential approach performs two calibrations: a flow calibration using head observations followed by the transport calibration using travel time observations. Both sets of observations are applied simultaneously in a single calibration run in the combined approach. Although, comparing estimated parameters with the initial synthetic reality values shows the better modeled results for both hydraulic conductivity and porosity while we apply combined approach. However time-cost along with difficulties to find a best weighs in combined approach imply that these differences are not significant.

Major Advisor

Amvrossios C. Bagtzoglou