Comparison of Different Configurations of Quantile Regression in Estimating Predictive Hydrological Uncertainty

Three different configurations of quantile regression as a statistical post processing technique to predict hydrological uncertainty in flood forecasts are investigated. The first configuration applies quantile regression of forecast errors conditioned on forecast discharge in original domain (QR-ORI) while the second configuration includes normal quantile transformation of forecast discharge and forecast errors prior to quantile regression (QR-NQT) to take into account the heteroscedasticity of the forecast errors. A weighted quantile regression is introduced as the third configuration (QR-WT) where weights are assigned during calibration of the quantile regression model to put more emphasis on higher flows, which is the regime of interest for flood forecasting systems. Probabilistic forecasts derived using these three different configurations are compared using different verification measures for the flood forecasting system of Sava River in Slovenia. Results show an improvement of QR-NQT over QR-ORI, largely depending on the size of the training and validation data set and the range of discharges in both data sets. It is also shown that QR-WT outperforms QR-ORI when the highest 25% discharges are considered.