Integrated Modeling of Carbon and Nitrogen Cycling at the Yakima River Basin


Peishi Jiang1* (, Zhi Li1, Glenn Hammond1, Katie Muller1, Hyun-Seob Song2, Vanessa Garayburu-Caruso1, Matt Kaufman1, Stephanie Fulton1, James Stegen1, Xingyuan Chen1, Tim Scheibe1


1Pacific Northwest National Laboratory, Richland, WA; 2University of Nebraska–Lincoln, Lincoln, NE



River corridors play important roles in watershed carbon and nitrogen cycling. This study seeks to quantify the cumulative impacts of river corridor hydrologic exchange flows, dissolved organic matter chemistry, and microbial activity on biogeochemical cycling. Leveraging the Advanced Terrestrial Simulator (ATS) and PFLOTRAN software coupled through the Alquimia interface from the IDEAS–Watersheds software ecosystem, the research team developed an integrated modeling framework that represents the river corridor as an integral part of a watershed using unstructured meshing in ATS. By seamlessly linking dynamic river flow processes and heterogeneous terrestrial inputs, such integrated modeling allowed for investigations into how variations in land use, hydrogeology, climate, and disturbances interact to control the spatial and temporal variability of aerobic respiration and denitrification processes across the Upper Yakima River Basin.

The research team further quantified the variability of organic carbon speciation and their impacts on carbon and nitrogen processing using Fourier-transform ion cyclotron resonance mass spectrometry measurements distributed across the Yakima River Basin. PFLOTRAN reaction sandbox was used to implement reaction networks and kinetics informed by carbon speciation and to scale up to watersheds. Model predicted discharge and aqueous concentrations of multiple species were confronted by observations collected at various locations to improve model parameters and process representations. Machine learning-based inverse mapping was employed for model parameter estimation. This study not only highlights the feasibility of modeling watershed carbon and nitrogen cycling at a high mechanistic level, but it also demonstrates that intentional, two-way iteration between modeling and experiments accelerates knowledge advancement.