Articles | Volume 374
Proc. IAHS, 374, 159–163, 2016
https://doi.org/10.5194/piahs-374-159-2016
Proc. IAHS, 374, 159–163, 2016
https://doi.org/10.5194/piahs-374-159-2016
 
17 Oct 2016
17 Oct 2016

Comparison of cross-validation and bootstrap aggregating for building a seasonal streamflow forecast model

Simon Schick et al.

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Cited articles

BMLFUW: Streamflow monitoring Austria, Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, http://ehyd.gv.at/, last access: 11 August 2016.
Breiman, L.: Bagging Predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1023/A:1018054314350, 1996a.
Breiman, L.: Out-of-bag estimation, https://www.stat.berkeley.edu/~breiman/OOBestimation.pdf (10 August 2016), 1996b.
CORINE: Corine Land Cover 2006 raster data, European Environment Agency, http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-3, last access: 11 August 2016.
E-OBS: Daily temperature and precipitation fields in Europe, ECA&D – European Climate Assessment & Dataset, http://www.ecad.eu/download/ensembles/ensembles.php, last access: 11 August 2016.
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Short summary
In water resources management, planning at the seasonal time scale is confronted with large uncertainties. Key variables are often unknown or hard to forecast, e.g. precipitation of the next three months. In the present study, we try to highlight some aspects concerning the development of a model faced with these uncertainties. Using the example of statistical streamflow forecasts, the results of the study indicate that the forecast accuracy is improved by the combination of several models.