A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers

Flow simulations of ephemeral rivers are often highly uncertain. Therefore, error models that can reliably quantify predictive uncertainty are particularly important. Existing error models are incapable of producing predictive distributions that contain >50% zeros, making them unsuitable for use in highly ephemeral rivers. We propose a new method to produce reliable predictions in highly ephemeral rivers. The method uses data censoring of observed and simulated flow to estimate model parameters by maximum likelihood. Predictive uncertainty is conditioned on the simulation in such a way that it can generate >50% zeros. Our method allows the setting of a censoring threshold above zero. Many conceptual hydrological models can only approach, but never equal, zero. For these hydrological models, we show that setting a censoring threshold slightly above zero is required to produce reliable predictive distributions in highly ephemeral catchments. Our new method allows reliable predictions to be generated even in highly ephemeral catchments. Plain Language Summary Many rivers cease to flow at various times. These rivers are difficult to model well, meaning that the models have a high level of uncertainty. There are no existing methods to correctly quantify the uncertainty in models of rivers that cease to flow >50% of the time. We propose a new method that can quantify the uncertainty of these models, even for rivers that cease to flow very often. Key Points Current hydrological error models cannot handle cases where >50% flow is zero We introduce a new error modeling method that uses data censoring of simulations and observations Our model produces reliable predictions even in highly ephemeral catchments