The Impacts of Landscape Nitrogen Dynamics on Surface Water Quality in Texas Watersheds

Nitrogen is important to support the demands for food and energy production of the global population, primarily through agriculture and burning fossil-fuels. Excessive reactive nitrogen 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 highlights the need to find a favorable balance of reactive nitrogen between human demands and environmental threats.

This research presents a nitrogen cycle modeling framework for integrating a terrestrial ecosystem model (i.e., Noah-MP with terrestrial carbon and nitrogen 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 nitrogen fluxes. We concentrate on the nitrogen 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 nitrogen dynamics, and connecting a routing model to describe nitrogen transport from land to streams. We also focus on human perturbations on nitrogen cycling, as described by Net Anthropogenic Nitrogen Inputs (NANI) data for Texas in a consistent and complex manner. Texas is one of the largest agriculture industries in the U.S., and we focus on coastal watersheds in Texas, which flow towards the Gulf of Mexico.

The feasibility of integrating the terrestrial nitrogen dynamics with the routing model for water flow and nitrogen will be demonstrated through the operation of the developed model with 36-year atmospheric forcing and NANI. Riverine nitrogen 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 nitrogen modeling.