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

Abstract

Historical estimates of seasonal snow mass are key to understanding snowmelt-driven streamflow and climate change impacts on mountain water resources. However, direct observations of snow mass are sparse in space and time, forcing most reconstructions to rely on snow models driven by uncertain meteorological inputs. While ground-based and satellite snow observations are commonly used to constrain these models, their potential is limited in data-scarce regions and before the onset of satellite monitoring. Here, we investigate the potential of streamflow observations as an additional source of information to improve historical snow mass reconstructions. 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 modeling chain can easily stand in the way. We also show that streamflow is most helpful in constraining catchment-aggregated properties of snow mass reconstructions, in particular catchment-aggregated melt rates. Future work should assess the potential of streamflow to constrain snow mass reconstruction under real-world conditions and investigate the added value of streamflow when combined with other snow data sources.