Snowpack-Driven Streamflow Predictability under Future Climate: Contrasting Changes across Two Western Canadian River Basins

Anthropogenic climate change-induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041-70 and 2071-2100 future periods against the1981-2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability. Significance StatementThe purpose of this study is to evaluate potential changes in seasonal streamflow predictability in relation to snowpack change under future climate. This is highly relevant because snowpack storage provides a means of predicting available freshet water supply, as well as peak flow events in cold regions. We use a machine learning model as an emulator of a hydrologic model in a testbed ensemble prediction system. Our results provide insights on hydroclimatic controls and interactions that affect future streamflow predictability across two river basins in western Canada. We conclude that besides snowpack, predictability depends on a number of other factors (basin/subbasin characteristics, streamflow variables, and future periods), and the loss of snowpack alone is not a sufficient condition for the reduction in streamflow predictability.

Automated summary

Anthropogenic climate change-induced snowpack loss is affecting the predictability of streamflow. This study evaluates the future changes in seasonal streamflow predictability in relation to snowpack change using a machine learning emulator. The results show contrasting patterns of change, with the predictive skills generally declining for mean flow and varying for maximum flow across different basins. The study concludes that snowpack loss alone is not sufficient to explain the reduction in streamflow predictability.