Implications of ensemble quantitative precipitation forecast errors on distributed streamflow forecasting

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs-one reliable and the other two affected by different kinds of precipitation forecast errors-generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the reliable ensemble QPFs are used. In addition, results underline (i) the importance of hindcasting to create an adequate set of data that span a wide range of hydrometeorological conditions and (ii) the sensitivity of the ensemble streamflow verification to the effects of basin initial conditions and the properties of the ensemble precipitation distributions. This study provides a contribution to the field of operational flow forecasting by highlighting a series of requirements and challenges that should be considered when hydrologic ensemble forecasts are evaluated.

Original languageEnglish (US)
Pages (from-to)69-86
Number of pages18
JournalJournal of Hydrometeorology
Issue number1
StatePublished - Feb 2010

ASJC Scopus subject areas

  • Atmospheric Science


Dive into the research topics of 'Implications of ensemble quantitative precipitation forecast errors on distributed streamflow forecasting'. Together they form a unique fingerprint.

Cite this