Post‐processing of ensemble forecasts in low‐flow period

For water supply, navigational, ecological protection or water quality control purposes, there is a great need in knowing the likelihood of the river level falling below a certain threshold. Ensemble streamflow prediction (ESP) based on simulations of deterministic hydrologic models is widely used to assess this likelihood. Raw ESP results can be biased in both the ensemble means and the spreads. In this study, we applied a modified general linear model post‐processor (GLMPP) to correct these biases. The modified GLMPP is built on the basis of regression of simulated and observed streamflow calculated on the basis of canonical events, instead of the daily values as is carried out in the original GLMPP. We conducted the probabilistic analysis of post‐processed ESP results falling below pre‐specified low‐flow levels at seasonal time scale. Raw ESP forecasts from the 1980 to 2006 periods by four different land surface models (LSMs) in eight large river basins in the continental USA are included in the analysis. The four LSMs are Noah, Mosaic, variable infiltration capacity and Sacramento models. The major results from this study are as follows: (1) a modified GLMPP was proposed on the basis of canonical events; (2) post‐processing can improve the accuracy and reduce the uncertainty of hydrologic forecasts; (3) post‐processing can help deal with the effect of human activity; and (4) raw simulation results from different models vary greatly in different basins. However, post‐processing can always remove model biases under different conditions. Copyright © 2014 John Wiley & Sons, Ltd.