Assessing the Effect of Data Resolution on Optimized Hydrograph Separation

Abstract. Hydrograph separation is centered on quantifying groundwater contributions to streamflow, yet the influence of monitoring data resolution remains insufficiently characterized. In this study, we assess how temporal resolution influences chemically constrained hydrograph separation. We apply HydrOHS, a multi-objective optimization framework that calibrates the physically-grounded Eckhardt Recursive Digital Filter using SC-centered mass balance, to four modeling scenarios capturing two spatial settings and two temporal resolutions. HydrOHS reconstructs SC end-members and simultaneously minimizes SC mismatch, enforces chemically consistent flow component ordering, and constrains peak baseflow index (BFI) behavior. The four modeling scenarios produce similar optimized BFI_max parameter values (0.80–0.89) and consistent downstream increases in baseflow contribution, indicating that temporal resolution (daily- vs 15 min monitoring frequency) exerts a secondary influence relative to hydrologic variability and missing flow periods. Higher resolution datasets provide additional detail but are more sensitive to zero-flow gaps, which degrades chemical reconstruction. Importantly, all optimized BFI_max estimates substantially exceed the suggested “default” BFI_max value for hard-rock aquifer systems, demonstrating that uncalibrated parameters underestimate baseflow. These findings highlight the importance of site-specific, chemically constrained calibration and show that daily resolution datasets may be sufficient for regional groundwater-recharge assessments.