Can we still predict the future from the past? Implementing non‐stationary flood frequency analysis in the UK

Abstract

The Environment Agency in England is investing £2.5 billion with the aim of reducing flood risk to at least 300,000 homes by 2020/21. Several of the schemes being considered are on rivers that have experienced an upsurge of flooding over recent years. Decisions on whether to invest and how high to build are usually made on the basis of stationary methods of flood frequency analysis that assume the probability of flood flows is unchanging over time. Following successive severe floods in Cumbria, trend tests and non-stationary flood frequency analysis techniques have been applied. These allow parameters of the frequency distribution to change over time or with some other covariate. The resulting estimates of flow, for the present day, were up to 55% higher than the stationary estimates at river gauges in north-west England. The results have been incorporated into the scheme appraisal process. A national analysis indicates that there is evidence of upward trends in peak flows at nearly a quarter of river flow gauges across Great Britain. Many rivers show an abrupt increase in flood flows in the late 1990s. Trends tend to occur in upland areas but they are also seen on some rivers across south-east England. K E Y W O R D Sclimate change, flood frequency estimation, non-stationary

A 2000-year documentary record of levee breaches on the lower Yellow River and their relationship with climate changes and human activities

“…For a longer time scale, examining the variability in breach frequency or intensity may help better understand the driving mechanism of hydrological changes of a river system (Faulkner et al, 2020). In this study, an event time series in terms of breach year (years characterized by one or more breaches), denoted as F , was first developed by using a binary expression with 1 denoting the presence and 0 the absence of breach(es) for a year.…”

Stationary vs non-stationary modelling of flood frequency distribution across northwest England

“…As a result, to the best of our knowledge, there is still a research gap for fully capturing the characteristics of non-stationary settings based on a generalized logistic (GLO) distribution model, by integrating various sequences of hydrological predictors. In this context, although limited studies have been undertaken to investigate the changes underlying the stochastic process of riverflow data in northwest England (Spencer et al 2018, Faulkner et al 2020, these have used the GEV model as the fitting distribution, while the GLO distribution is the recommended frequency model on most UK catchments. This discrepancy can have a major impact on the outcome of the analysis, and indeed further assists plan investment in flood alleviation in northwest England, where successive extreme flood events have occurred in recent years.…”

The effect of depth‐duration‐frequency model recalibration on rainfall return period estimates

“…All such studies suggest that the climate of the region is changing relatively quickly, raising questions over the suitability of stationary models for rainfall DDF estimates. Using trend tests and non‐stationary analysis, Faulkner et al (2020) found that non‐stationary flow estimates in north‐west England were up to 55% higher than stationary estimates. This could suggest that estimated return periods for the events considered here could become shorter and therefore that similar future events could occur more frequently than stationary analysis would suggest.…”

“…Serinaldi, Kilsby, and Lombardo (2018) conclude that no non‐stationary model can be justified or specified correctly without first identifying a clear, physical and deterministic cause for the potential presence of non‐stationarity. From a practical perspective, non‐stationary models always have more parameters than their stationary equivalents, hence there is additional scope for inaccurate parameterisation (Faulkner, Warren, Spencer, & Sharkey, 2020). This is a very real risk, given that the stationary FEH13 DDF model has 11 parameters.…”