ABSTRACT
The rapid pace and pervasiveness of landscape modification has made predicting watershed vulnerability to landscape change a key challenge for the twenty-first century. River ecosystems are, in particular, directly dependent on landscape structure and composition for their characteristic water and material budgets. Although it is widely acknowledged that landscape change poses serious risks to river ecosystems, quantification of past effects and future risks is problematic. Important issues of scale, hierarchy, and public investment intervene to complicate both assessment of current condition and the prediction of riverine responses to changes in landscape structure. In this paper we demonstrate how neural-net approaches to landscape change prediction can be coupled with river valley segment classification to provide a framework for integrated modeling and risk assessment across large-scale river ecosystems. Specifically we report on progress and techniques being employed in a collaborative risk assessment for the Muskegon River watershed, a large and valuable tributary of Lake Michigan.
