Ph.D., Biological Systems Engineering, Virginia Tech, 2021 Expected
B.S., Environmental Engineering, Cornell University, 2015
2017-present, Graduate Research Assistant, Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia
2013, 2015-2016, Wastewater Engineer, GHD Inc., Cazenovia, New York
2014-2015 Cornell Nutrient Management SPEAR Program, Dr. Quirine Ketterings, Ithaca New York
Teaching Assistant, Introduction to MATLAB, Cornell University, Ithaca, New York
Despite 30 years of effort, current and planned nutrient source reductions may be insufficient to achieve Chesapeake Bay water quality objectives by 2025, as called for in the TMDL (total maximum daily load). Chesapeake Bay partners have achieved 92% of the total phosphorus objectives but only 62% of the needed nitrogen reductions according to the latest load estimates from the Chesapeake Bay Model. Nearly all of the easily obtainable and inexpensive source reductions have been made. Most legacy N, defined as nitrogen that remains in a watershed for at least one year beyond its introduction, originates from land application of fertilizer or manure for agricultural production in excess of the immediate plant need. Thus, the excess is either exported with runoff or stored in groundwater. Legacy N introduces a critical time lag between changes in N application or land management and observable reductions in loads delivered to downstream waters and has been identified as a pressing issue that needs to be addressed in the Bay watershed. There is currently a disconnect between efforts to reduce N loads and water quality improvements, which can be explained by legacy N that is stored in groundwater and discharged years to decades later, depending on groundwater flowpath lengths. Thus, legacy N continues to be a source of impairment to surface water bodies even as contemporary N applications are reduced or eliminated. Currently there are few appropriate models that can evaluate the impacts of legacy N. I am addressing legacy N in groundwater by employing a novel coupled model to evaluate the extent of groundwater derived N, and, perhaps more critically, how managing N on landscapes propagates through the groundwater system.
Wagena, M.B., A.R. Sommerlot, E. Buell, G. Bhatt, and Z.M. Easton. 2019. Quantifying structural model uncertainty using a Bayesian multi model ensemble. Environmental Model Software. (In Rev