Comparison of Autoregressive Moving Average and State Space Methods for Monthly Time Series Modelling of Labrador and South-East Quebec River Flows

Carrisa Sparkes, Leonard M. Lye, Susan Richter

Time series data such as monthly stream flows can be modelled using time series methods and then used to forecast flows for short term planning. Two methods of time series modelling were reviewed and compared; the well-known autoregressive moving average (ARMA) method and the State- Space Time-Series (SSTS) method.  ARMA has been used in hydrology to model and simulate flows with good results and is widely accepted for this purpose.  SSTS modelling is a method that was developed in the 1990s and is relatively unused for modelling river flow time series data. The work described in this paper focuses on modelling the stream flows from basins of different sizes using these two time series modelling methods and comparing the results.  Three rivers in Labrador and South-East Quebec were modelled; the Romaine, Ugjoktok and Alexis Rivers.  These rivers are located in various areas of the study region, having different drainage aspects and differing basin sizes.  Both models were compared for accuracy of prediction, ease of software use and simplicity of model to determine the preferred time series methodology approach for modelling these rivers.