Sacha W Ruzzante, Tom Gleeson

Excessively low stream flows harm ecosystems and societies, so two key goals of low-flow hydrology are to understand their drivers and to predict their severity and frequency. We show that simple linear regressions can accomplish both goals across diverse catchments. We analyse 230 unregulated moderate to high relief catchments across rainfall-dominated, hybrid, snowmelt-dominated, and glacial regimes in British Columbia, Canada, with drainage areas spanning 5 orders of magnitude from 0.5 to 55,000 km2. We find that summer low flows are decreasing in rainfall-dominated and hybrid catchments and are dominantly driven by summer precipitation and temperature, and only weakly influenced by winter storage. We apply this understanding of drivers to create regression models to predict the minimum summer flow using monthly temperature and precipitation data. These models outperform distributed process-based models for every common goodness-of-fit metric; the performance improvement is mostly a result of abandoning the requirement to simulate all parts of the annual hydrograph. We use these regression models to reconstruct streamflow droughts, environmental flow threshold transgressions, and low flow anomalies from 1901-2022. We reproduce recent drying trends in rainfall-dominated and hybrid catchments, but also show that present conditions are comparable to those seen almost one hundred years ago. However, anomalously low flows last century were caused by severe precipitation deficits while current declines are being driven by rising summer temperatures during a period of above-average precipitation.