Improved endmember mixing analysis (EMMA): application to a snowmelt-dominated stream in northern Utah

Abstract. An endmember mixing analysis (EMMA) is a sophisticated hydrograph separation technique used to determine the primary water sources in a watershed and estimate their respective input over time. In a traditional EMMA approach, a principal component analysis (PCA) is used to identify endmember composition, and the retained principal component (PC) scores are used to calculate the fractional contributions of each endmember. This approach is based on the idea that the reduced dimensionality of the endmember data in just a handful of PCs contains the most useful information. While this approach does simplify the mixing calculation, it limits potential model complexity. We show that calculating endmember contributions using the original water chemistry data (tracer space) results in a more simplified and uniform approach than performing the calculation in PC-defined subspace. Additionally, we demonstrate an iterative approach to selecting the tracers and endmembers to create a more complex (and more representative) model. We applied EMMA to the upper Provo River watershed (262 km2), a snowmelt-dominated catchment in northern Utah, to test some potential improvements in the method. Five endmembers (quartzite groundwater, carbonate groundwater, mineral soil water, organic soil water, and snow) were identified for the watershed and differentiated using seven tracers (δ18O, δ2H, HCO3-, Si, Mg2+, K+, and Ca2+). We applied this approach in a well-defined workflow implemented in EMMALAB, a software application designed to perform EMMA on one or more stream locations in a catchment. The analysis showed that snow was the dominant endmember during spring runoff, contributing 38 % of flow on average, while quartzite groundwater contributed 60 % during baseflow. The iterative analysis for selecting endmembers and tracers is easily implemented through EMMALAB, allowing for a uniform and simplified approach to apply the complex mathematics behind EMMA for more accurate hydrograph separation calculations.