The Impacts of Human-Driven Inputs on Terrestrial and Riverine Nitrogen Fluxes in the United States

Nitrogen (N) is important to support the demands for food and energy production of the global population, primarily through agriculture and burning fossil-fuels. Excessive reactive N in terrestrial and freshwater systems, led by human activities, is causing wide-ranging environmental problems, such as harmful algal blooms, coastal dead zones and biodiversity loss. This underscores the need to find a favorable balance of reactive N between human demands and environmental threats. Previous studies presented the quantitative descriptions of N cycling based on various approaches and data, but large uncertainties of modeled N fluxes imply that our knowledge of sources, fates and exchanges of N within and between terrestrial and freshwater ecosystems is still deficient.

This poster presents a N cycle modeling framework for integrating a terrestrial ecosystem model (i.e., Noah-MP with terrestrial carbon and N dynamics, called Noah-MP-CN) with a vector-based streamflow routing model (i.e. the Routing Application for Parallel computation of Discharge, RAPID) to simulate landscape and riverine N fluxes. We concentrate on the N cycling from land to the river mouth, i.e., using a state-of-the-art land surface model under the atmospheric effects to simulate terrestrial N dynamics, and connecting a routing model to describe N transport from soil to streams. We also focus on human perturbations on N cycling, as described by Net Anthropogenic Nitrogen Inputs (NANI) data for the continental U.S. in a consistent and complex manner.

The feasibility of integrating the terrestrial N dynamics with the routing model for water flow and N will be demonstrated through the operation of the developed model with 36-year atmospheric forcing and NANI. This model will be demonstrated by comparing the modeled N budgets against past studies. Riverine N fluxes will be evaluated using the observed data for nitrate concentration and streamflow. Energy, hydrology and carbon cycles will also help to range the uncertainties of this model. For example, model output for evapotranspiration, runoff and net primary production will be evaluated based on field and remote-sensing observed data. This approach will help to describe the impacts on the surface water and energy balances of using the advanced N modeling.