Date of Award

Spring 5-2017

Document Type


Degree Name

Bachelor of Arts



First Advisor

Siobhan Fennessy


Nitrate runoff is one of the largest threats to aquatic systems globally, and denitrification has been proposed as a natural way to efficiently remove excess nitrates from ecosystems. In this study, we measured ambient denitrification rates, soil properties, and soil nutrients in a restored and natural Ohio wetland in August and October in an effort to better understand the relationships between wetland properties, season, and denitrification. We used these measured variables to test the performance of the Del Grosso DAYCENT NGAS sub-model to see if it was able to reliably predict denitrification rates across a hydrological gradient in heterogeneous wetland ecosystems. Denitrification rates ranged from 7.9 to 6,552 μg N2O-N m-2 h-1 at the restored site and 49.8 to 14,467 μg N2O-N m-2 h-1 at the natural site. Soil characteristics varied across seasons and trended towards higher denitrification, total soil carbon, total soil nitrogen, and soil extractable ammonia in the summer, though not all of these trends were statistically significant. Soil respiration and soil moisture were positively correlated with denitrification in August, but in October neither of these relationships were present. Mean total soil carbon (R2=0.54, p<0.01) and total soil nitrate (R2=0.62, p<0.01) were the strongest predictors of denitrification rates. The NGAS sub-model was able to accurately predict measured denitrification rates in 19 of our 24 samples. However, the model was unable to predict the measured rates in samples with high denitrification and low nitrate. These results emphasize the need for further testing and validation of predictive denitrification models in restored and natural freshwater wetlands. Reliable models may be able to give greater insight into the quality of ecosystem services that a site is able to support, the potential positive impacts that it can have on downstream water quality, and may aid in planning restoration at the landscape scale.