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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.

Item Type:	Refereed Article
Keywords:	maximum likelihood estimation, hydrological prediction, ephemeral rivers
Research Division:	Earth Sciences
Research Group:	Hydrology
Research Field:	Surface water hydrology
Objective Division:	Environmental Policy, Climate Change and Natural Hazards
Objective Group:	Understanding climate change
Objective Field:	Effects of climate change on Australia (excl. social impacts)
UTAS Author:	Bennett, JC (Mr James Bennett)
ID Code:	142662
Year Published:	2020
Web of Science® Times Cited:	10
Deposited By:	Oceans and Cryosphere
Deposited On:	2021-02-04
Last Modified:	2021-05-18
Downloads:	13 View Download Statistics