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.

Data sets

Streamflow monitoring Austria, Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft BMLFUW http://ehyd.gv.at/

Corine Land Cover 2006 raster data CORINE http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-3

Daily temperature and precipitation fields in Europe E-OBS http://www.ecad.eu/download/ensembles/ensembles.php

Digital Elevation Model over Europe EU-DEM http://www.eea.europa.eu/data-and-maps/data/eu-dem

Streamflow monitoring Bayern GKDB http://www.gkd.bayern.de/fluesse/abfluss/karten/index.php?thema=gkd&rubrik=fluesse&produkt=abfluss&gknr=0

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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.