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.

Changes in the magnitude and frequency of river flows have potential to alter sediment dynamics and morphology of rivers globally, but the direction of these changes remains uncertain. A lack of data across spatial and temporal scales limits understanding of river flow regimes and how changes in these regimes interact with river bank erosion and floodplain deposition. Linking characteristics of the flow regime to changes in bank erosion and floodplain deposition is necessary to understand how rivers will adjust to changes in hydrology from societal pressures and climatic change, particularly in snowmelt-dominated systems. We present a lidar dataset, intensive field surveys, aerial imagery and hydrologic analysis spanning 60 years, and spatial analysis to quantify bank erosion, lateral accretion, floodplain overbank deposition, and a floodplain fine sediment budget in an 11-km long study segment of the meandering gravel bed East River, Colorado, USA. Stepwise regression analysis of channel morphometry in nine study reaches and snowmelt-dominated annual hydrologic indices in this mountainous system suggest that sinuosity, channel width, recession slope, and flow duration are linked to lateral erosion and accretion. The duration of flow exceeding baseflow and the slope of the annual recession limb explain 59% and 91% of the variability in lateral accretion and erosion, respectively. This strong correlation between the rate of change in river flows, which occurs over days to weeks, and erosion suggests a high sensitivity of sedimentation along rivers in response to a shifting climate in snowmelt-dominated systems, which constitute the majority of rivers above 40° latitude.

Understanding the partitioning of snow and rain contributing to either catchment streamflow or evapotranspiration (ET) is of critical relevance for water management in response to climate change. To investigate this partitioning, we use endmember splitting and mixing analyses based on stable isotope (18O) data from nine headwater catchments in the East River, Colorado. Our results show that one third of the snow partitions to ET and 13% of the snowmelt sustains summer streamflow. Only 8% of the rainfall contributes to the summer streamflow, because most of the rain (67%) partitions to ET. The spatial variability of precipitation partitioning is mainly driven by aspect and tree density across the sub-catchments. Catchments with higher tree density have a higher share of snow becoming ET, resulting in less snow in summer streamflow. Summer streamflow did not contain more rain with higher rainfall sums, but more rain was taken up in ET.

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.

Deeper flows through bedrock in mountain watersheds could be important but lack of data to characterize bedrock properties and link flow paths to snow-dynamics limits understanding. To address data scarcity, we combine a previously published integrated hydrologic model of a snow-dominated, headwater basin with a new method for dating baseflow age using dissolved gas tracers SF, N, Ar. The original flow model produces shallow groundwater flow (median depth 6 m), very young stream water and is unable to reproduce observed SF concentrations. To match the observed gas data, bedrock permeability is increased to allow a larger fraction of deeper groundwater flow (median depth 110 m). Results indicate that interannual variability in baseflow age (3-12 y) is dictated by the volume of seasonal interflow. Deeper groundwater flow remains stable (11.7±0.7 y) as a function of the ratio of recharge to bedrock hydraulic conductivity (R/K), where recharge is dictated by long-term climate and land use. With sensitivity experiments, we show that information gleaned from gas tracer data to increase bedrock hydraulic conductivity effectively moves this alpine basin away from shallow, topographically controlled groundwater flow with baseflow age relatively insensitive to water inputs (high R/K), and closer toward recharge-controlled conditions, in which a small shift toward a drier future with less snow accumulation will alter the groundwater flow system and increase baseflow age (low R/K). Work stresses the need to explore alternative methods characterizing bedrock properties in mountain basins to better quantify deeper groundwater flow and predict their hydrologic response to change.

Stable isotopes of water are important tracers in hydrologic research for understanding water partitioning between vegetation, groundwater, and runoff, but are rarely applied to large watersheds with persistent snowpack and complex topography. We combined an extensive isotope dataset with a coupled hydrologic and snow isotope fractionation model to assess mechanisms of isotopic inputs into the soil zone and implications on recharge dynamics within a large, snow-dominated watershed of the Upper Colorado River Basin. Results indicate seasonal isotopic variability and isotope lapse rates of net precipitation are the dominant control on isotopic inputs to the basin. Snowpack fractionation processes account for <5% annual isotope influx variability. Isotopic fractionation processes are most important in the shrub-dominated upper montane. Effects of isotopic fractionation are less important in the low-density conifer forests of the upper subalpine due to vegetative shading, low aridity, and a deep, persistent snowpack that buffers small sublimation losses. Melt fractionation can have sub-seasonal effects on snowmelt isotope ratios with initial snowmelt depleted but later snowmelt relatively enriched in heavy isotopes through the isotopic mass balance of the remaining snowpack, with the efficiency of isotopic exchange between ice and liquid water declining as snow ablation progresses. Hydrologic analysis indicates maximum recharge in the upper subalpine with wet years producing more isotopically depleted snowmelt (1-2‰ reduction in d18O) through reduced aridity when energy-limited. The five-year volume-weighted d18O in this zone (18.2±0.4‰) matches groundwater observations from multiple deep wells, providing evidence that the upper subalpine is a preferential recharge zone in mountain systems.

Patterns of watershed nitrogen (N) retention and loss are shaped by how watershed biogeochemical processes retain, biogeochemically transform, and lose incoming atmospheric deposition of N. Loss patterns represented by concentration, discharge, and their associated stream exports are important indicators of watershed N retention patterns because they reveal hysteresis patterns (i.e. return to initial state) or one-way transition patterns (i.e. new steady state) that provide insight into watershed conditions driving long term stream trends. We examined the degree to which Continental U.S. (CONUS) scale deposition patterns (wet and dry atmospheric deposition), vegetation trends, and stream trends can be potential indicators of watershed N-saturation and retention conditions, and how watershed N retention and losses vary over space and time. By synthesizing changes and modalities in watershed nitrogen loss patterns based on stream data from 2200 U.S. watersheds over a 50 year record, our work characterized a new hysteresis conceptual model based on factors driving watershed N-retention and loss, including hydrology, atmospheric inputs, land-use, stream temperature, elevation, and vegetation. Our results show that atmospheric deposition and vegetation productivity groups that have strong positive or negative trends over time are associated with patterns of stream loss that uniquely indicate the stage of watershed N-saturation and reveal unique characteristics of watershed N-retention hysteresis patterns. In particular, regions with increasing atmospheric deposition and increasing vegetation health/biomass patterns have the highest N-retention capacity, become increasingly N-saturated over time, and are associated with the strongest declines in stream N exports—a pattern that is consistent across all land cover categories. In particular, the second largest factor explaining watershed N-retention was in-stream temperature and dissolved organic carbon concentration trends, while land-use explained the least amount of variability in watershed N-retention. Our CONUS scale investigation supports an updated hysteresis conceptual model of watershed N-retention and loss, providing great value to using long-term stream monitoring data as indicators of watershed N hysteresis patterns.

A coupled hydrologic and snowpack stable water isotope model assesses controls on isotopic inputs across a large, mountainous basin. The most depleted isotope conditions occur in the upper subalpine where snow accumulation is high and rainfall is low. Snowmelt evolution over time indicates isotopic enrichment is not dictated by melt fractionation but is determined by elevation which controls the amount, phase and isotopic mass of spring precipitation coincident with the ablation period. With respect to snowpack kinetic fractionation, its effect on snowmelt is a balance between energy and snow-availability. It is highest above treeline and in the shrub-dominated upper montane where vegetation shading is low, while deep snowpack and conifer forests limit the influence of kinetic fractionation in the subalpine. Wet years reduce the effects of snowpack fraction on snowmelt across the basin, except in the lower montane where added snowfall bolsters water-limited conditions.

The North American Monsoon occurs July-September bringing significant rainfall to Colorado River headwater basins. This rain may buffer streamflow deficiencies caused by reductions in snow accumulation. Using a data-modeling framework, we explore the importance of monsoon rain in streamflow generation over historic conditions in an alpine basin. Annually, monsoon rain contributes 18{plus minus}7% water inputs, generates 10{plus minus}6% streamflow and increases water yield 3{plus minus}2% the following year. The bulk of rain supports evapotranspiration in lower subalpine forests. However, rains have the potential to produce appreciable streamflow at higher elevations where soil storage, forest cover and aridity are low; and rebounds late season streamflow 64{plus minus}13% from simulated reductions in snowpack as a function of monsoon strength. Interannual variability in monsoon efficiency to generate streamflow declines with low snowpack and high aridity, implying the ability of monsoons to replenish streamflow in a warmer future with less snow accumulation will diminish.

The U.S. Department of Energy’s (DOE) East River community observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow-dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. Led by the Watershed Function Science Focus Area (SFA), the ER has both long-term and spatially-extensive observations paired with experimental campaigns. The Watershed Function SFA, led by Berkeley Laboratory, includes researchers from over 30 organizations who conduct cross-disciplinary process-based investigations and mechanistic modeling of watershed behavior in the ER. The data generated at the ER are extremely heterogeneous, and include hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data that together comprise an unprecedented collection of data and value-added products within a mountainous watershed, across multiple spatiotemporal scales, compartments, and life zones. Within 5 years of data collection, these datasets have already revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over sixty ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.