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

Dawson, C.W; Mount, Nick J.; Abrahart, R.J; Shamseldin, A.Y.

Abstract

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.

Citation

Dawson, C., Mount, N. J., Abrahart, R., & Shamseldin, A. (2012). Ideal point error for model assessment in data-driven river flow forecasting. Hydrology and Earth System Sciences, 16(8), https://doi.org/10.5194/hess-16-3049-2012

• Journal Article Type: Article
• Publication Date: Jan 1, 2012
• Deposit Date: Jan 30, 2015
• Publicly Available Date: Jan 30, 2015
• Journal: Hydrology and Earth System Sciences
• Print ISSN: 1027-5606
• Electronic ISSN: 1027-5606
• Publisher: European Geosciences Union
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 8
• DOI: https://doi.org/10.5194/hess-16-3049-2012
• Public URL: https://nottingham-repository.worktribe.com/output/1008597
• Publisher URL: http://www.hydrol-earth-syst-sci.net/16/3049/2012/hess-16-3049-2012.html