Short Lead-Time Streamflow Forecasting by Machine Learning Methods, with Climate Variability Incorporated

Streamflow fluctuates as a result of different atmospheric, hydrologic, and morphologic mechanisms governing a river watershed. Variability of meteorological variables such as rainfall, temperature, wind, sea level pressure, humidity, and heating, as well as large scale climate indices like the Arctic Oscillation, Pacific/North American Pattern, North Atlantic Oscillation, and El-Niño Southern Oscillation play a role on the availability of water in a given basin. In this study, data generated by the NOAA Global Forecasting System (GFS) model, climate fluctuations, and observed data from meteo-hydrorologic stations are used to forecast daily streamflows. Three machine learning methods are used for this purpose: support vector regression (SVR), Gaussian process (GP), and Bayesian neural network (BNN) models, and the results are compared with the multiple linear regression (MLR) model. Lead-time for forecasting varies from 1 to 7 days. This study has been applied to a small coastal watershed in British Columbia, Canada. The results show that when the lead-time increases, climate indices such as the Arctic Oscillation and North Atlantic Oscillation become more important on influencing the flow variability. Model comparisons show the BNN model to slightly outperform the GP and SVR models and all three models perform better than the MLR model.