Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models

Variations in climate conditions during recent decades in British Columbia, Canada have occurred coincident to significant changes in streamflow conditions in the province. In the current study, the ability of empirical downscaling models to resolve these changes is investigated using ensemble neural networks forced with synoptic-scale atmospheric conditions. Five-day averages of streamflow data from 21 watersheds in the region are modelled using atmospheric data from the NCEP/NCAR reanalysis project as inputs. Ability of the downscaling models to predict streamflow and changes in streamflow between 1975–1986 and 1987–1998 is evaluated using a combination of model performance statistics, comparisons between long-term averages, and results from non-parametric statistical tests. While performance varied between systems, results suggest that empirical downscaling models for streamflow are capable of predicting changes in streamflow observed during recent decades using only large-scale atmospheric conditions as model inputs. Based on comparisons between stepwise linear regression and neural network models, the latter approach is recommended, particularly when trying to model systems with complex non-linear and interactive relationships between inputs and outputs. The use of ensemble averaging as a part of the modelling process is investigated and a number of recommendations are made with respect to this methodology.