Data and Code Archive for "Optimal Baseflow Separation through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters" (Q7335)

This is the data archive accompanying following paper: Mei, Y.; Wang, D.; Zhu, J.; Tang, G.; Cai, C.; Shen, X.; Hong, Y.; Zhang, X. (2024): Optimal Baseflow Separation through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters; submitted to Water Resources Research. Please refer to the paper for details on data sources, methods and algorithms. Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance, turbidity) constitutes an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow separation, three key components are needed: the tracer, the method to estimate tracer concentration in different flow components, and the empirical baseflow filter. However, a comprehensive evaluation of the various combinations of these components, especially on a larger scale, is currently lacking in literature. Therefore, our study assembles 16 hybrid baseflow filters from two tracers, two concentration estimation methods, and four empirical baseflow filters, and evaluated their performance in baseflow separation and producing two long-term baseflow signatures for 1,100 catchments in the Continental United States. Our results suggest that specific electrical conductance is a superior tracer to turbidity for baseflow separation. Additionally, using monthly maximum and minimum values to represent tracer concentration in flow components produces better separation than using a power function relationship between flow rate and concentration. The four empirical baseflow filters offer a similar level of performance, regardless the other options used. Yet, some of these filters produce inconsistent results in calculating the baseflow signatures for the catchments. Our analysis shed light on the optimization of hybrid baseflow filters for the accurate quantification of baseflow contribution.