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The rapid pace 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, 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 and its ecological interactions with Lake Michigan.

The Muskegon River Ecological Modeling System (MREMS) links the Land Transformation Model (LTM) to a series of standard hydrologic, loading, and biological models via a GIS-based valley segment classification framework. Models currently linked in MREMS include MODFLOW, HEC-HMS, GWLF, and a number of regional biological assessment models. The MRI-VSEC river reach classification system, a GISproduct developed for the Fisheries Division of the Michigan Department of Natural Resources, is used to define spatial rules for the parameterization and spatial integration of component models. The result is an open modeling system capable of predicting future (and past) states of the landscape, and then evaluating likely changes in hydrology, chemistry, and biological integrity/productivity in a spatially explicit framework. Integrated modeling of the Muskegon has already identified key areas of the watershed at risk from future development.
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Document Type: Research Article

Publication date: 01 January 2004

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