Watershed Dynamics and Evolution (WaDE) Science Focus Area Modeling Crosscut: Modeling the Effects of Hyporheic Zone Processes on Stream Oxygen Dynamics
Authors
Jesus D. Gomez-Velez* (gomezvelezjd@ornl.gov), Saubhagya Rathore, Phong Le, Scott L. Painter, Eric Pierce
Institutions
Oak Ridge National Laboratory, Oak Ridge, TN
URLs
Abstract
The Modeling Crosscut Activity of the Watershed Dynamics and Evolution Science (WaDE) science focus area is developing a virtual watershed capability focused on watershed hydrology, water temperature, and stream metabolism. This capability supports WaDE’s research themes and is critical for efforts to gain a transferable understanding of hydro-biogeochemical regimes in watersheds with heterogeneous land covers.
The team summarizes its ongoing efforts to improve understanding of whole-stream metabolism and its conceptual representation for modeling and data interpretation. Specifically, researchers use a new multiscale model for reactive transport in stream corridors to investigate how hyporheic exchange flows and heterotrophic respiration in the hyporheic zone interact to influence oxygen dynamics in stream channels. The multiscale model associates a subgrid model for hyporheic transport and reactions with each computational grid cell in a channel network model. The approach differs from the classical transient storage model in that it represents a diversity of advective flowpaths characterized by the hyporheic travel time distribution. Researchers configure the multiscale reactive transport capability in the ATS code to represent the key processes controlling oxygen dynamics: (1) gross primary production (GPP) and respiration (Rc) in the channel, (2) oxygen exchange with the atmosphere, (3) hyporheic exchange and advective transport, and (4) aerobic respiration (Rs) in the hyporheic zone. Rs kinetics are represented as a dual Monod process. Researchers use this conceptualization to identify dimensionless parameters that collectively control oxygen dynamics.
Researchers then performed a meta-analysis of publicly available datasets across the conterminous United States to constrain the variability of these dimensionless quantities in natural systems. Finally, the new model and the meta-analysis results were used to perform a detailed global sensitivity analysis and Bayesian inverse modeling to assess potential biases arising from neglecting mass transfer limitations, as is commonly done in the single-station method for estimating GPP and ER from time-resolved oxygen measurements.