Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables
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Hydrol. Earth Syst. Sci., 25, 5315–5336,2021
Hydrol. Earth Syst. Sci., 22, 2471–2485,2018
Hydrol. Earth Syst. Sci., 20, 4881–4894,2016
Hydrol. Earth Syst. Sci., 19, 2491–2504,2015
Hydrol. Earth Syst. Sci., 16, 4517–4530,2012
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, https://doi.org/10.5194/hess-16-1085-2012, 2012.
Brier, G. W.: Verification of Forecasts Expressed in Terms of Probability, Mon. Weather Rev., 78, 1–3, https://doi.org/10.1175/1520-0493(1950)078<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, https://doi.org/10.1175/BAMS-D-12-00081.1, 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, https://doi.org/10.1016/0169-2070(86)90048-8, 1986.