Mountainous watersheds are characterized by variability in functional traits, including vegetation, topography, geology, and geomorphology, which together determine nitrogen (N) retention, and release. Coal Creek and East River are two contrasting catchments within the Upper Colorado River Basin that differ markedly in total nitrate (NO3-) export. The East River has a diverse vegetation cover, sinuous floodplains, and is underlain by N-rich marine shale, resulting in a three to twelve times greater total NO3- export relative to the conifer-dominated Coal Creek. While this can partly be explained by the larger size of the East River, the distinct watershed traits of these two catchments imply different mechanisms controlling the aggregate N-export signal. A causality analysis shows biogenic and geogenic processes were critical in determining NO3- export from the East River catchment. Stable isotope ratios of NO3- (δ15NNO3 and δ18ONO3) show the East River catchment is a strong hotspot for biogeochemical processing of NO3- at the soil-saprolite interface and within the floodplain prior to export. By contrast, the conifer-dominated Coal Creek retained nearly all (~97 %) atmospherically-deposited NO3-, and its export was controlled by catchment hydrological traits (i.e., snowmelt periods and water table depth). The conservative N-cycle within Coal Creek is likely due to the abundance of conifer trees, and a smaller riparian region, retaining more NO3- overall and reduced processing prior to export. This study highlights the value of integrating isotope systematics to link watershed functional traits to mechanisms of watershed element retention and release.

Data standardization can enable data reuse by streamlining the way data are collected, providing descriptive metadata, and enabling machine readability. Standardized open-source data can be more readily reused in interdisciplinary research that requires large amounts of data, such as climate modeling. Despite the importance given to both FAIR (Findable, Accessible, Interoperable, Reusable) data practices and the need for open-source data, a remaining question is how community data standards and open-source data can be adopted by research data providers and ultimately achieve FAIR data practices. In an attempt to answer this question, we used newly created water quality community data reporting formats and applied them to open-source water quality data. The development of this water quality data format was curated with several other related formats (e.g., CSV, Sample metadata reporting formats), aimed at targeting the research community that have historically published water quality data in a variety of formats. The water quality community data format aims to standardize how these types of data are stored in the data repository, ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem). Adoption of these formats will also follow FAIR practices, increase machine readability, and increase the reuse of this data. We applied this community format to open-source water quality data produced by the Watershed Function Scientific Focus Area (WFSFA), a large watershed study in the East River Colorado, which involves many national laboratories, institutions, scientists, and disciplines. In this presentation, we provide a demonstration of a relatively efficient process for converting open-source water quality data into a format that adheres to a community data standard. We created examples of water quality data translated to the reporting formats that demonstrated the functionality of these data standards; descriptive metadata and sample names, streamlined data entries, and increased machine readability were products of this translation. As the community data standards are integrated within the WFSFA data collection processes, and ultimately all data providers of ESS-DIVE, these steps may enable interdisciplinary data discovery, increase reuse, and follow FAIR data practices.

Quantifying flow and transport from hillslopes is vital for understanding surface water quality, but remains obscure because of limited subsurface measurements. A recent combination of water mass balance over a single year with the transmissivity feedback model for a lower montane hillslope in the East River watershed (Colorado) left large uncertainties in transmissivities and predicted fluxes. Because snowmelt drives subsurface flow on this hillslope, improved constraints on the transmissivity profile were obtained by optimizing flux predictions over years having large differences in precipitation minus evapotranspiration. The optimized field-scale hydraulic properties combined with water table elevations predict groundwater discharges that are consistent with wide ranges of snowmelt. As snowmelt rapidly raises the water table, solutes released primarily through bedrock weathering are largely transported out of the hillslope via its highly transmissive soil. Such pulsed water and solute exports along the soil are minimized during snow drought years. Although solute concentrations generally are lower in soils relative to the underlying weathering zone, solute exports during high recharge occur predominantly via soil because of its enlarged transmissivities under snowmelt-saturated conditions. In contrast, this shallow pathway is negligible when recharge and water table elevations are low. The multiyear calibrated subsurface properties combined with updated pore water chemistry continue to show that the weathering zone is the primary source of base cations and reactive nitrogen released from the hillslope. Subsurface export predictions can now be obtained for wide ranges of snowmelt based on measurements of water table elevation and profiles of pore water chemistry.