Evaluation of bias-correction methods for ensemble streamflow volume forecasts

T. Hashino, A. A. Bradley, S. S. Schwartz

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)

Abstract

Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa), issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

Original languageEnglish
Pages (from-to)939-950
Number of pages12
JournalHydrology and Earth System Sciences
Volume11
Issue number2
DOIs
Publication statusPublished - 2007

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)

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