Preprints
https://doi.org/10.5194/hess-2022-401
https://doi.org/10.5194/hess-2022-401
 
06 Jan 2023
06 Jan 2023
Status: this preprint is currently under review for the journal HESS.

Modelling non-stationary flood frequency in England and Wales using physical covariates

Duncan S. Faulkner1, Sean Longfield2,3, Sarah Warren1, and Jonathan A. Tawn4 Duncan S. Faulkner et al.
  • 1JBA Consulting, Skipton, BD23 3FD, UK
  • 2Environment Agency, Leeds, LS11 9AT, UK
  • 3Department of Geography, University of Lincoln, Lincoln, LN6 7TS, UK
  • 4Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK

Abstract. Non-stationary methods of flood frequency analysis are widespread in research but rarely implemented by practitioners who manage flood risk. One reason for this may be that research papers on non-stationary statistical models tend to focus on model fitting rather than extracting the sort of results needed by designers and decision makers. It can be difficult to extract useful results from non-stationary models that include stochastic covariates for which the value in any future year is unknown. Examples of such covariates include rainfall, temperature or indices of fluctuations of atmospheric pressure.

We explore the motivation for including such covariates, whether on their own or in addition to a covariate based on time. We set out a method for expressing the results of non-stationary models, and their uncertainty, as an integrated flow estimate, which removes the dependence on a particular value of the covariates. This can be defined either for a particular year or over a longer period of time. The methods are illustrated by application to a set of 375 river gauges across England and Wales. We find annual rainfall to be a useful covariate at many gauges, sometimes in conjunction with a time-based covariate.

For estimating flood frequency in future conditions, we advocate exploring hybrid approaches that combine the best attributes of non-stationary statistical models and simulation models that can represent the impacts of changes in climate and river catchments.

Duncan S. Faulkner et al.

Status: open (until 03 Mar 2023)

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Duncan S. Faulkner et al.

Duncan S. Faulkner et al.

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Short summary
Because the climate and river catchments are changing, when planning flood management we should consider methods that allow for the probabilities of floods to change over time. These methods can also allow for trends and fluctuations in flood probability associated with changes in physical variables such as annual rainfall. We explore different approaches for doing this and set out a method for extracting useful results from such statistical models. We present results across England and Wales.