Abstract
Abstract. Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of skill at forecasting stations that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal hindcasts of streamflows at three test stations in the Columbia River basin in the US Pacific Northwest. An improvement in forecast skill of 5 to 10 % is found for two test stations. The streamflows at the third station are less affected by ENSO and no change in forecast skill is found here.
Highlights
The ensemble streamflow prediction (ESP) forecasting method is a common way to produce seasonal outlooks of river volumes
The skill of the forecasts was assessed in terms of root mean square error (RMSE) of the ensemble mean, Brier score (BS) and continuous ranked probability score (CRPS)
The performance of the subsampler selecting historical years from the original ESP based on climate mode similarity was first evaluated without the addition of resampled time series
Summary
The ensemble streamflow prediction (ESP) forecasting method is a common way to produce seasonal outlooks of river volumes. Hay et al (2009) applied a climate-mode-dependent adjustment of hydrologic model parameters Another pre-processing alternative is to generate synthetic input time series by random resampling of monthly MAP and MAT from historical years that have similar climate index values (Werner et al, 2004). Instead of a weighting scheme, Hamlet and Lettenmaier (1999) used a selection of ESP traces according to a classification of historical years based on ENSO and PDO climate indices Their results showed an improved specificity of the ensemble forecast, the classification leads to a reduction of ensemble traces, because the number of historical years in each class is obviously less than the original number of ensemble traces. The newly generated traces augment the ensemble up to the original number of traces and all ensemble traces are weighted This preserves the statistical properties of the ESP ensemble and avoids loss of forecast skill due to reduction of (effective) ensemble size.
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