AutoVL: Automated streamflow separation for changing catchments and climate impact analysis

Abstract

The separation of streamflow into fastflow and slowflow components has been historically ambiguous, with existing separation methods like the Lyne-Hollick (LH) algorithm facing challenges due to subjective parameter choices. Here, we address this issue by developing the AutoVL algorithm which objectively and automatically partitions streamflow for no parameter input. AutoVL uses iterative statistical models, including a Signal Reconstructor for fastflow and an autoregressive moving-average (ARMA) model for slowflow, to estimate key hydrologic parameters. The algorithm couples the two models to iteratively estimate these parameters and to accurately separate streamflow. When applied to the Harvey River, Dingo Road station data, AutoVL identified significant seasonal and long-term variations in hydrologic parameters, reflecting the possible influence of climate change altering the temporal dynamics of catchment responses. The algorithm highlighted strongly coupled changes in infiltration and decay rates from altered streamflow patterns, offering a clearer understanding of streamflow responses to climate change. This performance suggests that AutoVL provides a more reliable, objective, efficient, and standard method for streamflow separation compared to previous approaches, enabling more accurate and confident hydrological modeling. By providing objective, dynamic insights into catchment behavior, AutoVL offers a promising tool for climate change studies and streamflow analysis.