Benchmarking Different Approaches for Harnessing Predictability in Climate and Hydrologic Initial Conditions for Seasonal Runoff Prediction

Over recent decades, a number of forecasting centers around the world have offered seasonal streamflow predictions, using methodologies that span a wide range of data requirements and complexity. In the western United States, two primary approaches have been adopted for operational purposes: (i) development of regression equations between future streamflow and in situ observations (e.g. rainfall, snow water equivalent), and (ii) ensemble hydrologic model simulations that combine initial watershed moisture states with historically observed weather sequences for the forecast period (e.g. Ensemble Streamflow Prediction, ESP). Neither of these operational methodologies makes use of analyzed or forecasted climate information, which might increase the skill of seasonal predictions. There is a need to better understand not only the potential benefits of additional predictor information (such as climate) but also potential advances that may be gained from more complex methods, such as hybrid dynamical/statistical approaches.

In this work, we provide a systematic intercomparison of various seasonal streamflow forecasting techniques, including: (1) a dynamical approach based on conceptual hydrologic modeling and ESP, (2) statistical regression using climate information and/or initial hydrologic conditions, (3) an ESP trace weighting scheme based on analog climatic conditions, and (4) hierarchical combination of dynamical and statistical forecasts (i.e. hybrid). Climate information is taken from the NCEP CFSR and CFSv2 reanalysis and reforecast datasets. These methods and data are tested for predicting spring runoff volumes at case study basins located in the US Pacific Northwest, and results obtained for several initialization times are evaluated in terms of accuracy, probabilistic skill and reliability. Preliminary results suggest that hierarchical approaches that merge multiple predictions provide a powerful framework that can leverage different predictability sources at different times of year. In this presentation, we also outline ideas for an international seasonal hydrologic prediction intercomparison experiment.