Can streamflow observations constrain snow mass reconstructions? Lessons from two synthetic numerical experiments

Abstract. Historical snow mass estimates are key to understanding snowmelt-driven streamflow and climate change impacts on snow water resources. However, snow mass observations are scarce, and SWE reconstructions rely largely on snow models forced with meteorological inputs. Ground-based and satellite observations are often used to constrain the typically high uncertainty of modeled snow mass reconstructions, but their constraining potential is limited in data-scarce regions and prior to the onset of satellite monitoring. Here, we suggest using streamflow information as an additional information source to better reconstruct snow mass. We introduce an inverse hydrological modeling framework that selects realistic snow mass realizations based on the accuracy of their streamflow response. Before real-world application, we test the framework in two synthetic experiments. Our results demonstrate that streamflow has the potential to constrain snow mass reconstructions, but that non-uniqueness in the snow-streamflow relationship and uncertainties in the inverse modelling chain can easily stand in the way. We also show that streamflow is most helpful in estimating catchment-aggregated properties of snow mass reconstructions, in particular catchment-aggregated melt rates. Future work should assess the potential of streamflow-constrained snow mass reconstruction under real-world conditions and investigate the added value of streamflow when combined with other snow data sources.