Stream chemistry changes in response to snowmelt, but does not typically reflect thechemistry of the snowpack. This suggests that flow processes between snowmelt and streamsystem, such as interactions with the soil and bedrock, have an important control on waterchemistry and highlight the complex flow pathways from the snowpack to stream. To investigateflow processes in the upper Provo River watershed, northern Utah, we sampled three sites on theriver ~20 times per year during 2016 and 2017. The sites, from highest elevations to lowest wereSoapstone, Woodland, and Hailstone, corresponding to locations of active stream gauges. Toidentify possible water sources to the stream during snowmelt, water samples were taken forsnow, ephemeral streams, soil water, lake, and spring water. To investigate potential impacts ofmineralogy, samples were taken for dust, soil and bedrock. The upper Provo River showeddistinct temporal variation in filtered (<0.45 microns) stream water for 87Sr/86Sr, dissolvedorganic carbon (DOC), silica (Si), and Lead (Pb) during the snowmelt season. The watershed hasdistinct 87Sr/86Sr ratios for bedrock (0.7449)

An endmember mixing analysis (EMMA) is a hydrograph separation technique used to identify and quantify stream source contributions, but the error within the results of the analysis itself can be difficult to quantify. Employing EMMA to accurately quantify these contributions is particularly important for critical watersheds that supply water to large populations, such as montane watersheds. We applied EMMA to the Provo River, a nested catchment with three monitoring locations in northern Utah, to understand the limitations and potential improvements that could be made to EMMA. Four main endmembers (quartzite groundwater, soil water, snow and carbonate groundwater) were identified for the watershed and differentiated using the conservative tracers δ18O, δ2H, Si, HCO3-, Mg2+, K+, and Ca2+. In a traditional EMMA approach, a principal components analysis (PCA) is used to identify endmembers for a single location in a watershed, and the principal component (PC) scores are used to calculate the fractional contributions of each endmember. However, we found that calculating the fractional contributions of the endmembers in tracer space resulted in less error in the calculations compared to performing the calculation in PC defined space (U-space). Performing the mixing in tracer space with four endmembers showed that during spring runoff, snow was the main endmember with inputs ranging from 23 – 66% for the highest part of the watershed and 14 – 60% for the lowest part of the watershed. During baseflow, the stream was dominated by groundwater with contributions ranging from 23 – 60% quartzite groundwater for the upper part of the watershed and 30 – 57% carbonate groundwater for the lower part of the watershed. The amount of error present in the results depended on the scale of the catchment and the number of endmembers included, with more error in downstream locations relative to upstream locations. The nested catchment approach is a further improvement on traditional EMMA because it allows for identification of missing endmembers and error analysis for characterizing stream chemistry in several locations in a complex watershed.