Integrating Planning, Forecasting, and Watershed Level Ecological Risk Assessment Techniques: A Test in the Eastern Cornbelt Plains EcoregionEPA Grant Number: R824769
Title: Integrating Planning, Forecasting, and Watershed Level Ecological Risk Assessment Techniques: A Test in the Eastern Cornbelt Plains Ecoregion
Investigators: Gordon, Steven I. , Ward, Andy
Current Investigators: Gordon, Steven I. , Ward, Andy , White, Dale A
Institution: The Ohio State University
EPA Project Officer: Hiscock, Michael
Project Period: October 1, 1995 through September 1, 1998
Project Amount: $445,000
RFA: Water and Watersheds (1995) Recipients Lists
Research Category: Water and Watersheds , Water
Description:The objectives of this research are to test the relationships between biological conditions of streams and the nature and distribution of human activities on the watershed; to demonstrate methods for linking physical models of urban and agricultural impacts on runoff volume and runoff quality; to define the relationships between physical model forecasts and the biological conditions of streams; and to integrate all of the findings into an expert system to be used by planners.
Using selected watersheds in the Eastern Cornbelt Plains Ecoregion of Ohio, we have been examining the relationships between biological quality as measured by the Index of Biological Integrity (IBI) and indicators of stress as measured by land use and channel/riparian zone conditions. We are also undertaking watershed modeling which utilizes remotely sensed data, the ADAPT field scale process model, and a GIS to assess flow, sediment, nutrient and pesticide discharges on small watersheds with a predominantly agricultural land use. The University of Georgia riparian zone model which simulates surface and subsurface hydrology, nutrient dynamics, and plant growth will be linked to this model and the USEPA Storm Water Management Model (SWMM) to allow explicit consideration of the impacts of riparian zone on agricultural runoff and urban runoff. Additional refinements of the statistical modeling will be based on the comparison with detailed analysis of several watersheds. Detailed characterizations of the riparian zone using remote sensing techniques will be used to assess the relative impacts of riparian zone versus upland characteristics. Regional level statistical modeling will be refined over the next year including the riparian zone analyses. Linkage of the three simulation models will be performed using data for at least two. Predicted results from the linked simulation models will be statistically compared with observed stream flow data (quantity and quality). We will use the combined results to build a decision-analysis tool tied to land-use planning applications.
End-products (scientific papers, dissertations, and the model software) are anticipated within about two years.