Simulating nitrogen processes in a mountainous watershed using an integrated hydrology and reactive transport model
Abstract
Hydrogeochemical exports from mountainous watersheds aggregate the complex interactions between hydrological, biogeochemical and vegetation processes across the different compartments. Process-based high-resolution models - such as the novel integrated hydrology and reactive transport model implemented in the Advanced Terrestrial Simulator (ATS) - make it possible to develop a mechanistic understanding of how these interactions play out spatially and temporally to produce the observed exports. These models however are challenging to parameterize the complex processes, with the need of high-resolution datasets derived from a variety of sources.
In this study, we use ATS to simulate the integrated hydrology and reactive transport processes in the Lower Triangle Region of the East River, CO watershed that control water and nitrogen exports. Detailed watershed characterization data obtained from field campaigns, geophysical and geological models, as well as from machine learning and process-based models are used to generate model inputs that capture the heterogeneous distribution of properties in the watershed. These include lidar-based vegetation land cover, soil thickness, soil organic carbon and nitrogen content, shale organic carbon content, and mineralogical compositions. The geochemical model focuses on processes affecting nitrogen exports including mineral weathering and redox reactions. The position of the weathering fronts across the watershed is initialized based on the results of long-term spinup hydrological simulations. The model is calibrated against river discharge and solute data, and is used to quantify the seasonal and annual variations of hydrogeochemical exports to the riverine environment under changing climate drivers and other disturbances. We argue that the combination of data ingestion and processing workflow tools, with data- and process-based models will ultimately enable developing a quantitative understanding of the controls on watershed function.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H44F..02X