A dual-pass error-correction technique for forecasting streamflow

SummaryThis study reports the development and testing of a “dual-pass” method for correcting slowly varying errors in simulations of streamflow. Error correction is a form of data assimilation used to improve streamflow forecasts. The dual-pass method is ideally suited for catchments with long-lasting shifts in runoff efficiency that the hydrologic model poorly simulates. In a first pass, the simulation is rescaled (i.e. multiplicative correction is applied), based on the cumulative error over the prior 365 days. In a second pass, a correction is added to the adjusted series, based on the error from the most recent timestep. When tested on 330 Australian and 183 United States catchments, the dual-pass approach improved the median four-measure validation skill score from 0.83 to 0.89 for the GR4J model and from 0.56 to 0.79 for a naïve coefficient model. The majority of improvement comes from the second pass (the short-memory component). The magnitudes of correction at each pass are controlled by two tuneable parameters; a global sensitivity analysis determined that satisfactory performance could be had without tuning of the long-memory (first pass) error-correction parameter. For most catchments, the use of the long-memory error-correction neither degrades nor significantly improves performance. International hydrological modeling datasets have relatively fewer catchments with slowly varying errors than might be encountered in operational forecasting environments, therefore, there is a need for better identification and study of such problem catchments.

► We improve streamflow forecasts by using information about recent errors.
► Our novel technique adds information about slowly varying biases.
► Ours is the first study to use hundreds of catchments, with several models over many years.
► Short-memory error correction is essential and long-memory helps the worst performers.