Expectile-based hydrological modelling for uncertainty estimation: Life after mean

Predictions of hydrological models should be probabilistic in nature. Our aim is to introduce a method that estimates directly the uncertainty of hydrological simulations using expectiles, thus complementing previous quantile-based direct approaches. Expectiles are new risk measures in hydrology. They are least square analogues of quantiles and can characterize the probability distribution in much the same way as quantiles do. To this end, we propose calibrating hydrological models using the expectile loss function, which is consistent for expectiles. We apply our method to 511 basins in contiguous US and deliver predictive expectiles of hydrological simulations with the GR4J, GR5J and GR6J hydrological models at expectile levels 0.500, 0.900, 0.950 and 0.975. An honest assessment empirically proves that the GR6J model outperforms the other two models at all expectile levels. Great opportunities are offered for moving beyond the mean in hydrological modelling by simply adjusting the objective function.