Estimation of uncertainty in flood forecasts - a comparison of methods

Boelee, L., Lumbroso, D. M., Samuels, P. G. and Cloke, H. L. ORCID: https://orcid.org/0000-0002-1472-868X (2019) Estimation of uncertainty in flood forecasts - a comparison of methods. Journal of Flood Risk Management, 12 (S1). e12516. ISSN 1753-318X doi: 10.1111/jfr3.12516

Abstract/Summary

The scientific literature has many methods for estimating uncertainty, however, there is a lack of information about the characteristics, merits and limitations of the individual methods, particularly for making decisions in practice. This paper provides an overview of the different uncertainty methods for flood forecasting that are reported in literature, concentrating on two established approaches defined as the ensemble and the statistical approach. Owing to the variety of flood forecasting and warning systems in operation, the question ‘which uncertainty method is most suitable for which application’ is difficult to answer readily. The paper aims to assist practitioners in understanding how to match an uncertainty quantification method to their particular application using two flood forecasting system case studies in Belgium and Canada. These two specific applications of uncertainty estimation from the literature are compared, illustrating statistical and ensemble methods, and indicating the information and output that these two types of methods offer. The advantages, disadvantages and application of the two different types of method are identified. Although there is no one ‘best’ uncertainty method to fit all forecasting systems, this review helps to explain the current commonly used methods from the available literature for the non-specialist.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/81470
Item Type	Article
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Wiley-Blackwell
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