A comparative study of the magnitude, frequency and distribution of intense rainfall in the United Kingdom

Rodda, John C.; Little, Max A.; Rodda, Harvey J. E.; McSharry, Patrick E.

doi:10.1002/joc.2024

Cited by 15 publications

(9 citation statements)

References 19 publications

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“…Both Min et al (2011) and Westra et al (2013) concluded that a statistically significant increase in the annual maximum daily precipitation can be detected on a global basis. For Northern and Central Europe a number of recent studies exists for various regions; Belgium (Ntegeka and Willems 2008), Czech Republic (Kysely 2009), Denmark (Gregersen et al 2013a), Germany/Poland (Lupikasza et al 2011), Sweden (Bengtsson andRana 2013) and UK (Rodda et al 2010). Depending on season, region within the country and analysed indices, they all detect changes in heavy rainfall.…”

Section: Introductionmentioning

confidence: 99%

Long term variations of extreme rainfall in Denmark and southern Sweden

Gregersen

Madsen²,

Rosbjerg

et al. 2014

Clim Dyn

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Section: Introductionmentioning

confidence: 99%

Long term variations of extreme rainfall in Denmark and southern Sweden

Gregersen

Madsen²,

Rosbjerg

et al. 2014

Clim Dyn

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“…Jenkins et al (2008) also report a decrease in rainfall during the summer season and an increase in winter rainfall, with a particular increase of high-rainfall events in winter. As Rodda et al (2010) point out, it is difficult to discern whether or not the observed changes in extreme rainfall pattern can be linked to human activities, as the signal for change can be quite variable. The appropriate methodology and approaches used in trend studies are still a debated issue: the same direction of a signal in a data series can be identified by different methods, but these might give contrasting indications when it comes to evaluating the statistical and practical significance of the estimated signal; see Lins and Cohn (2005) for a full commentary on this.…”

Section: Prosdocimi Et Al: Non-stationarity In the Ukmentioning

confidence: 99%

Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK

Prosdocimi

Kjeldsen

Svensson

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. When designing or maintaining an hydraulic structure, an estimate of the frequency and magnitude of extreme events is required. The most common methods to obtain such estimates rely on the assumption of stationarity, i.e. the assumption that the stochastic process under study is not changing. The public perception and worry of a changing climate have led to a wide debate on the validity of this assumption. In this work trends for annual and seasonal maxima in peak river flow and catchment-average daily rainfall are explored. Assuming a two-parameter log-normal distribution, a linear regression model is applied, allowing the mean of the distribution to vary with time. For the river flow data, the linear model is extended to include an additional variable, the 99th percentile of the daily rainfall for a year. From the fitted models, dimensionless magnification factors are estimated and plotted on a map, shedding light on whether or not geographical coherence can be found in the significant changes. The implications of the identified trends from a decisionmaking perspective are then discussed, in particular with regard to the Type I and Type II error probabilities. One striking feature of the estimated trends is that the high variability found in the data leads to very inconclusive test results. Indeed, for most stations it is impossible to make a statement regarding whether or not the current design standards for the 2085 horizon can be considered precautionary. The power of tests on trends is further discussed in the light of statistical power analysis and sample size calculations. Given the observed variability in the data, sample sizes of some hundreds of years would be needed to confirm or negate the current safety margins when using at-site analysis.

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“…Using the same methodology, we updated that study utilizing data for the period 1961 to 2006 for the same gauges, or from those nearby (Rodda et al, 2010). The purpose of updating the study was to investigate the changes in the patterns of extreme rainfalls for the two periods.…”

Section: Quantifying the Risk Of Extreme Eventsmentioning

confidence: 99%

“…A wide range of practical applications rely on information about the likelihood of extreme rainfall events (Rodda, 1967(Rodda, , 1972Hand et al, 2004). This information can be obtained by investigating the relationship between magnitude, frequency and distribution of intense rainfall (Coles, 2001).…”

Section: Introductionmentioning

confidence: 99%

Quantifying flood risk of extreme events using density forecasts based on a new digital archive and weather ensemble predictions

McSharry

Little

Rodda³

et al. 2013

Quart J Royal Meteoro Soc

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Non-coastal flood events in the UK are usually associated with extreme rainfall and can last from minutes to weeks. Efficient management and mitigation of flood risk requires accurate and reliable precipitation forecasts as inputs to flood risk models. We constructed an archive of British Rainfall data from 1866 to the present day to improve our understanding of historical extreme rainfall events. The relationship between record rainfall and flooding is nonlinear and uncertain, implying that probabilistic forecasts of rainfall are required. We developed an objective classification scheme of extreme rainfall events consisting of eight types, analysed extreme rainfall events and produced probabilistic forecasts by combining statistical techniques with the outputs of ensemble predictions from a numerical weather predictions model.

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A comparative study of the magnitude, frequency and distribution of intense rainfall in the United Kingdom

Cited by 15 publications

References 19 publications

Long term variations of extreme rainfall in Denmark and southern Sweden

Long term variations of extreme rainfall in Denmark and southern Sweden

Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK

Quantifying flood risk of extreme events using density forecasts based on a new digital archive and weather ensemble predictions

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