This study evaluates whether combined use of streamflow and isotope tracer performance metrics can improve representation of simulated streamflow-generating processes within a large river basin, the Athabasca watershed, to inform calibration of a process-based, distributed hydrologic model.
Flow-based performance metrics were found to be sensitive to processes influencing streamflow volume and timing, but insensitive to internal flow paths and storage volumes. Although somewhat less reliable than flow metrics, isotope tracer performance metrics are found to be most sensitive to processes influencing mixing and water age, and appreciably responsive to many other processes. We demonstrate that process-based hydrologic models for rivers such as the Athabasca River cannot be optimally calibrated using streamflow metrics alone, as such optimizations cannot tune parameters or process representations to which the objective function is insensitive. Importantly, isotope tracers have demonstrable value for informing process-based hydrologic model optimization by providing a window into the sub-surface black box within complex regional-scale simulations.