North-Central Section (44th Annual) and South-Central Section (44th Annual) Joint Meeting (11–13 April 2010)

Paper No. 48
Presentation Time: 8:30 AM-12:00 PM

MODELING LAND USE CHANGE EFFECTS ON SEDIMENT DISCHARGE IN THE MAUMEE RIVER


ZMIJEWSKI, Kirk, Department of Environmental Sciences, University of Toledo, 2081 Bancroft Ave, Toledo, OH 43606 and BECKER, Doris, Department of Environmental Sciences, University of Toledo, 2801 West Bancroft Ave, Toledo, OH 43606, kirk.zmijewski@rockets.utoledo.edu

The Maumee watershed in northwest Ohio has the largest drainage of any of the great lakes watersheds. Due to high sediment and nutrient input the lower portion of the Maumee watershed has been designated as an Area of Concern by the US EPA. Non point source pollution contributes substantially to nutrient loading in the Maumee River watershed, which in turn discharges into the Maumee bay. The Maumee Bay experienced significant algal blooms in the 1970s and 80s, which were reduced until the last decade by the reduction of nutrients. In recent years, algal blooms have occurred in the Maumee Bay during the late summer and early fall months. Of significant concern is Microcystis, a potentially harmful cyanobacteria. Previous studies have indicated that reducing the sediment load is likely to increase growth stress for Microcystis, and this increased stress shoul result in lower magnitude of blooms in the western basin of Lake Erie. A Soil and Water Assessment Tool (SWAT) model of the Maumee watershed was developed to examine the effect of different land use and tillage practices on sediment and nutrient flow into the western basin from the Maumee. Two scenarios based on NLCD land use classifications (from 1992, and 2001) were developed and calibrated against data obtained at the Waterville, OH monitoring station for a five year time period corresponding to the land use data. The model was calibrated to flow volume, sediment concentration, as well as N and P concentrations. In addition to NLCD land use data, SRTM topography, STATSGO soil classifications were used to generate the model. Each scenario was run for the years1990-2009, using meteorology from local weather stations. The results from these two scenarios were compared over the duration of the model to identify differences cased solely by changes in land use,