Journal article

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

QJ Wang, JC Bennett, DE Robertson, M Li

Water Resources Research | American Geophysical Union | Published : 2020


©2020. American Geophysical Union. All Rights Reserved. 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 ..

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University of Melbourne Researchers


Funding Acknowledgements

This research has been supported by the Water Information Research and Development Alliance (WIRADA) between the Bureau of Meteorology and CSIRO Land and Water. Streamflow data have been retrieved from the Bureau of Meteorology's hydrological reference station data set, freely available at Catchment delineations were created with the Bureau of Meteorology's geofabric, freely available from Rainfall and potential evaporation data were retrieved from the gridded Australian Water Availability Project (AWAP) data set: Derived time series of rainfall and potential evaporation used in this study are available from the authors on request. A full set of Matlab and C++ code used for this study are available on request; license conditions apply.