Rivers and adjacent landscapes are important parts of the Critical Zone. They serve as integrators of the hydrologic and biogeochemical cycles, constituting the primary pathways for geochemical exports from watersheds. Thus, they exert primary control on determining the downstream river water quality. The net geochemical export of metals and nutrients from watersheds depends strongly upon hydrological exchanges and biogeochemical transformations at the terrestrial–aquatic interfaces and riparian corridors. It is critical to link hydrological and biogeochemical processes in riparian corridors so as to understand how Critical Zone watershed processes determine water availability and quality for sustainable management. The riparian corridors include various subsystems, such as hyporheic zones, meanders, wetlands and lagoons, all of which impact river water quality. Further, these subsystems demonstrate distinct biogeochemical potential depending upon their hydrologic connectivity to the main channel. Recognizing the importance of linked hydrological and biogeochemical processes in a riparian corridor and inherent challenges, the watershed science and Critical Zone communities are working to make significant improvements in our understanding and prediction, by using advanced computational resources and novel, continuous and high-resolution field data acquired using various sensor networks and platforms such as ground, drones and satellites. However, several hurdles must be overcome to improve the predictive capability of riparian corridor processes at the large scale. Here we invite theoretical and data-driven contributions that can advance the predictive understanding of riparian corridor processes. Topics of interest include but are not limited to (1) new approaches aggregating a spectrum of data sources to improve process understanding across spatiotemporal scales in the riparian corridors; (2) reactive transport modeling to understand hydrobiogeochemical dynamics; (3) the quantification of fine-scale processes’ contribution to the integrated, transient export of water, nitrogen, carbon and metals; (4) the use of machine learning and artificial intelligence to advance predictive understanding of riparian corridor processes, and (5) uncertainty quantification and inherent data challenges.
Rivers and adjacent landscapes are important parts of the Critical Zone. They serve as integrators of the hydrologic and biogeochemical cycles, constituting the primary pathways for geochemical exports from watersheds. Thus, they exert primary control on determining the downstream river water quality. The net geochemical export of metals and nutrients from watersheds depends strongly upon hydrological exchanges and biogeochemical transformations at the terrestrial–aquatic interfaces and riparian corridors. It is critical to link hydrological and biogeochemical processes in riparian corridors so as to understand how Critical Zone watershed processes determine water availability and quality for sustainable management. The riparian corridors include various subsystems, such as hyporheic zones, meanders, wetlands and lagoons, all of which impact river water quality. Further, these subsystems demonstrate distinct biogeochemical potential depending upon their hydrologic connectivity to the main channel. Recognizing the importance of linked hydrological and biogeochemical processes in a riparian corridor and inherent challenges, the watershed science and Critical Zone communities are working to make significant improvements in our understanding and prediction, by using advanced computational resources and novel, continuous and high-resolution field data acquired using various sensor networks and platforms such as ground, drones and satellites. However, several hurdles must be overcome to improve the predictive capability of riparian corridor processes at the large scale. Here we invite theoretical and data-driven contributions that can advance the predictive understanding of riparian corridor processes. Topics of interest include but are not limited to (1) new approaches aggregating a spectrum of data sources to improve process understanding across spatiotemporal scales in the riparian corridors; (2) reactive transport modeling to understand hydrobiogeochemical dynamics; (3) the quantification of fine-scale processes’ contribution to the integrated, transient export of water, nitrogen, carbon and metals; (4) the use of machine learning and artificial intelligence to advance predictive understanding of riparian corridor processes, and (5) uncertainty quantification and inherent data challenges.