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Ideal point error for model assessment in data-driven river flow forecasting

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posted on 22.08.2013, 14:55 authored by Christian DawsonChristian Dawson, Nick J. Mount, Robert J. Abrahart, Asaad Y. Shamseldin
When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking.

History

School

  • Science

Department

  • Computer Science

Citation

DAWSON, C.W. ... et al, 2012. Ideal point error for model assessment in data-driven river flow forecasting. Hydrology and Earth System Sciences, 16 (8), pp.3049-3060.

Publisher

Published by Copernicus Publications on behalf of the European Geosciences Union. (© Author(s))

Version

VoR (Version of Record)

Publication date

2012

Notes

This work is distributed under the Creative Commons Attribution 3.0 License.

ISSN

1027-5606;1607-7938

Language

en