Paper No. 1
Presentation Time: 9:00 AM-6:30 PM

CALIBRATING A GIS-BASED SOIL-EROSION MODEL FOR A SMALL URBAN WATERSHED: A SEDIMENTARY STUDY OF LILY POND, YOUNGSTOWN, OHIO


NORTON, M.S. and MATTHEUS, C.R., Geological and Environmental Sciences, Youngstown State University, One University Plaza, Youngstown, OH 44555, msnorton@student.ysu.edu

Lily Pond is a man-made, 3-acre catch basin located within Mill Creek Metro Parks in Youngstown, Ohio. The pond and associated spillway were created in 1896. By 1974 the pond had largely filled in with sediment, mostly detrital material derived from sheet and rill erosion of surrounding hillsides, prompting a sediment excavation project that re-graded the pond to a uniform water depth of five feet. Detailed assessments of sediment accumulation thereafter are utilized to calibrate an erosion model for the contributing watershed for application in future land-management decision making.

The erosion model for the 16-acre Lily Pond watershed is based on the Universal Soil Loss Equation, incorporating published soil, elevation, rainfall, and land-cover datasets. A sediment-thickness map of post-1974 accretion was constructed from excavation-survey and modern bathymetry measurements. Cores were collected in an even distribution across the pond, sub-sampled, and analyzed for organic content and particle size. In addition to providing insight into spatial sedimentation patterns through time, sedimentologic data yielded correction factors for the exclusion of organic materials in the quantification of clastic sediment accumulation.

Sediment type and accretion vary across Lily Pond, conforming to model predictions of high sediment-input locations. Organic materials make up less than 3% of the total sediment volume. The adjusted weight of inorganic sediment sequestered since 1974 approximates 700 US tons, over twice as much as our model prediction. The land-cover factor presents a likely candidate for future investigation and ongoing research into the vegetation history of the watershed could help fine-tune model precision.