Abstract

Both concentration-discharge relation and end-member mixing analysis were explored to elucidate the connectivity of hydrologic and hydrochemical processes using chemical data collected during 2006–2008 at Happy Isles (468 km2), Pohono Bridge (833 km2), and Briceburg (1873 km2) in the snowmelt-fed mid-Merced River basin, augmented by chemical data collected by the USGS during 1990–2014 at Happy Isles. Concentration-discharge (C-Q) in streamflow was dominated by a well-defined power law relation, with the magnitude of exponent (0.02–0.6) and R2 values (p < 0.001) lower on rising than falling limbs. Concentrations of conservative solutes in streamflow resulted from mixing of two end-members at Happy Isles and Pohono Bridge and three at Briceburg, with relatively constant solute concentrations in end-members. The fractional contribution of groundwater was higher on rising than falling limbs at all basin scales. The relationship between the fractional contributions of subsurface flow and groundwater and streamflow (F-Q) followed the same relation as C-Q as a result of end-member mixing. The F-Q relation was used as a simple model to simulate subsurface flow and groundwater discharges to Happy Isles from 1990 to 2014 and was successfully validated by solute concentrations measured by the USGS. It was also demonstrated that the consistency of F-Q and C-Q relations is applicable to other catchments where end-members and the C-Q relationships are well defined, suggesting hydrologic and hydrochemical processes are strongly coupled and mutually predictable. Combining concentration-discharge and end-member mixing analyses could be used as a diagnostic tool to understand streamflow generation and hydrochemical controls in catchment hydrologic studies.

Citation

Liu F., Conklin M.H., Shaw G.D (2017): Insights into hydrologic and hydrochemical processes based on concentration‐discharge and end‐member mixing analyses in the mid‐Merced River Basin, Sierra Nevada, California. Water Resources Research 53, 832–850. DOI: 10.1002/2016WR019437

This Paper/Book acknowledges NSF CZO grant support.