Articles | Volume 19, issue 9
Research article
25 Sep 2015
Research article |  | 25 Sep 2015

Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

F. Hoss and P. S. Fischbeck

Related subject area

Subject: Rivers and Lakes | Techniques and Approaches: Uncertainty analysis
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Cited articles

Alexander, M., Harding, M., and Lamarche, C.: Quantile Regression for Time-Series-Cross-Section-Data, Int. J. Stat. Manage. Syst., 4, 47–72, 2011.
Bogner, K., Pappenberger, F., and Cloke, H. L.: Technical Note: The normal quantile transformation and its application in a flood forecasting system, Hydrol. Earth Syst. Sci., 16, 1085–1094,, 2012.
Brier, G. W.: Verification of Forecasts Expressed in Terms of Probability, Mon. Weather Rev., 78, 1–3,<0001:VOFEIT>2.0.CO;2, 1950.
Demargne, J., Wu, L., Regonda, S. K., Brown, J. D., Lee, H., He, M., Seo, D.-J., Hartman, R., Herr, H. D., Fresch, M., Schaake, J., and Zhu, Y.: The Science of NOAA's Operational Hydrologic Ensemble Forecast Service, B. Am. Meteorol. Soc., 95, 79–98,, 2013.
Hsu, W. and Murphy, A. H.: The attributes diagram A geometrical framework for assessing the quality of probability forecasts, Int. J. Forecast., 2, 285–293,, 1986.
Short summary
This paper further develops the method of quantile regression (QR) to generate probabilistic river stage forecasts. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48h and the forecast error 24 and 48h before as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the CRPS.