Groundwater hysteresis increasingly decouples flowing network length from streamflow as snow shifts to rain

Abstract. Flowing stream networks expand and contract in response to dynamic groundwater levels. Field studies generally associate greater flowing network length (L) with higher streamflow (Q), but this neglects potential hysteresis caused by nonequilibrium groundwater flow after rain and snowmelt. Using a new version of the Distributed Hydrology Soil Vegetation Model (DHSVM), we predict that groundwater hysteresis may decouple L from Q across large (> 100 %) variations in Q. Groundwater hysteresis contributes to the spatial reconfiguration of active flowpaths and changes to hillslope-riparian hydrological connectivity, which can manifest as a network length scaling anomaly relative to the best-fit power law. In a 27 km2 snowy volcanic watershed, seasonal anomalies in measured stream ionic concentration indicate an outsized contribution from longer subsurface flowpaths during recession, supporting our L-Q hysteresis hypothesis and refining our model calibration. The model can reproduce observed stream network elasticity (from field surveys), and the predicted network length anomaly mirrors seasonal anomalies in measured stream ionic concentration (r=-0.92), suggesting that the model can capture seasonal changes in the spatial configuration of groundwater convergence and streamflow generation. A warmer climate is expected to cause a partial transition from snow to rain resulting in flashier streamflow, but our simulations predict that seasonal groundwater hysteresis would dampen storm-scale stream network elasticity, thereby significantly increasing L-Q hysteresis on daily to monthly timescales (p < 0.01). Conceptual models of stream networks should consider the potential effects of groundwater hysteresis, especially in a changing environment. More broadly, our investigation highlights how spatially distributed process-based hydrological modeling can reveal emergent hydrological behaviors that are not necessarily apparent from sparse field data.