Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data.

Assessing regional hydrology and water quality implications of large-scale biofuel feedstock production in the Upper Mississippi River Basin.

A recent U.S. Department of Energy study estimated that more than one billion tons of biofuel feedstock could be produced by 2030 in the United States from increased corn yield, and changes in agricultural and forest residue management and land uses.