New Software to Analyze How Extremes Change Over Time

Gilleland, Eric; Katz, Richard W.

doi:10.1029/2011eo020001

Cited by 190 publications

(116 citation statements)

References 2 publications

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“…The 2000s saw the autumn T10 total remain relatively high, but the summer total increased greatly compared to previous decades. Note that the magnitude of the 0.95 and 0.99 quantiles [Gilleland and Katz, 2011] are strongly correlated (p < 0.01) with the decadal T10 totals (Table 6). It is notable that the [2008] have argued that an increase in winter precipitation intensity through the twentieth century was indicative of a long-term trend, whereas fluctuations in summer precipitation intensity were more consistent with interdecadal variability than with any overall trend.…”

Section: Heavy Falls Of Rainmentioning

confidence: 90%

A homogenous daily rainfall record for the Radcliffe Observatory, Oxford, from the 1820s

Burt

Howden

2011

Water Resources Research

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A record of daily rainfall totals from 1827 at the Radcliffe Observatory, Oxford, United Kingdom, is presented. Changing instrumentation over the period and homogenization of the record are described. Brief analysis of the record is provided, including reference to the monthly record from 1767. Most heavy falls of rain occur in summer. The twentieth century tendency toward wetter winters was not accompanied by any significant increase in heavy falls of rain. There were very few heavy falls in summer during the 1990s but a large number in autumn. Preliminary analysis suggests that there are more heavy downpours in summers when there is reduced storminess in the North Atlantic region.

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Section: Heavy Falls Of Rainmentioning

confidence: 90%

A homogenous daily rainfall record for the Radcliffe Observatory, Oxford, from the 1820s

Burt

Howden

2011

Water Resources Research

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“…Both models were fit with the R package "extRemes" [36] using the Maximum Likelihood Estimation (MLE) method and profile likelihood was used to obtain the 95% confidence intervals (CI) to capture the uncertainty associated with the EV model parameters. MLE is one of the most commonly used for parameter estimation for EV models [37] and is valid for samples of size n > 25 [38].…”

Section: Block Maxima and Peak Over Threshold Methodsmentioning

confidence: 99%

Assessing the Impact of Climate Change and Extreme Value Uncertainty to Extreme Flows across Great Britain

Collet

Beevers

Prudhomme

2017

Water

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Floods are the most common and widely distributed natural risk, causing over £1 billion of damage per year in the UK as a result of recent events. Climatic projections predict an increase in flood risk; it becomes urgent to assess climate change impact on extreme flows, and evaluate uncertainties related to these projections. This paper aims to assess the changes in extreme runoff for the 1:100 year return period across Great Britain as a result of climate change using the Future Flows Hydrology database. The Generalised Extreme Value (GEV) and Generalised Pareto (GP) models are automatically fitted for 11-member ensemble flow series available for the baseline and the 2080s. The analysis evaluates the uncertainty related to the Extreme Value (EV) and climate model parameters. Results suggest that GP and GEV give similar runoff estimates and uncertainties. From the baseline to the 2080s, increasing estimate and uncertainties is evident in east England. With the GEV the uncertainty attributed to the climate model parameters is greater than for the GP (around 60% and 40% of the total uncertainty, respectively). This shows that when fitting both EV models, the uncertainty related to their parameters has to be accounted for to assess extreme runoffs.

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“…Extreme value theory assumes that data are independent and identically distributed (Coles, 2001;Gilleland and Katz, 2011;Katz, 2010Katz, , 2013Cheng et al, 2014). To test for non-stationarity in the expected value we perform separate Mann-Kendall trend tests (Mann, 1945;Kendall, 1976;Gilbert, 1987) at a significance level of α = 0.05 (Zhang et al, 2004) for the extreme value series of each meteorological indicator.…”

Section: Dealing With Non-stationarity and Dependencymentioning

confidence: 99%

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

Schlögl

Laaha

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend the use of conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

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New Software to Analyze How Extremes Change Over Time

Cited by 190 publications

References 2 publications

A homogenous daily rainfall record for the Radcliffe Observatory, Oxford, from the 1820s

A homogenous daily rainfall record for the Radcliffe Observatory, Oxford, from the 1820s

Assessing the Impact of Climate Change and Extreme Value Uncertainty to Extreme Flows across Great Britain

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

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