Memory decay curves describe streamflow dynamics across Europe at multiple timescales

Streamflow is typically autocorrelated, and the degree to which prior conditions influence river flow informs how we predict future conditions and can drive temporal clustering of hydrological extremes. We introduce a memory decay curve that describes streamflow memory (i.e. autocorrelation) based on two components: initial strength and persistence. These curves effectively summarize the dynamics of catchment memory at monthly to multi-annual timescales, allowing for large-sample inter-catchment comparisons.

We fit these memory decay curves to streamflow measurements from thousands of EStreams stations across Europe from 1980-2021. From these curves, we distinguish four basic memory archetypes based on the combination of strong (or weak) memory and long (or short) persistence. These archetypes exhibit distinct geographic patterns across Europe, with strong and long memory most present in regions with large aquifers or deep bedrock.

Streamflow memory at different timescales shows varied connections to different surface, subsurface, and climate characteristics. We use a random forest model to predict memory from these characteristics at multiple timescales, with the highest skill for seasonal and the lowest skill for yearly predictions. Surface-related features (e.g. topography) influence model predictions at shorter timescales, whereas subsurface feature importances increase with lag time; climate features, in particular aridity, are important across all timescales. We also compare the memory present in observation-based data to the memory produced by modelled streamflow for Europe to understand how well these dynamics are represented in modelled data. The memory decay curves presented in this study demonstrate the presence of hydrologic memory in European catchments at timescales from months to years and can improve the understanding and prediction of streamflow dynamics.