Streamflow forecasting at large time scales using statistical models

Abstract Traditional methods for streamflow forecasting include statistical models, which have outperformed machine learning algorithms at large timescales (i.e., monthly and annual) in the absence of informative exogenous variables. This chapter presents an overview as well as the theory of statistical models and methods for forecasting of streamflow time series. We show how statistical models (exponential smoothing and autoregressive fractionally integrated moving average models) can be used for streamflow forecasting, and we present large-scale studies where statistical models are utilized, followed by explanations for their observed behavior. We also outline, in this chapter, the role of large-scale studies in advancing hydrological forecasting in practice.