Journal article

On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data-Driven Models

Feifei Zheng, Holger R Maier, Wenyan Wu, Graeme C Dandy, Hoshin V Gupta, Tuqiao Zhang



Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the developme..

View full abstract

University of Melbourne Researchers


Awarded by National Natural Science Foundation of China

Awarded by Australian Research Council through the Centre of Excellence for Climate System Science

Funding Acknowledgements

Zheng acknowledges funding support from The National Natural Science Foundation of China (grant 51708491), and Gupta acknowledges partial support from the Australian Research Council through the Centre of Excellence for Climate System Science (grant CE110001028). We gratefully appreciate Keith Beven, Saman Razavi, and the other two anonymous reviewers for their constructive comments, which help us to improve the quality of this paper significantly. We also gratefully acknowledge data for the 432 U.S. catchments provided by Thibault Mathevet, which can be also accessed through, with details of this data set given in Data for the Australian catchments are synthesized based on data given in Chiew et al. (2009) and have been submitted as supporting information.