A regional model for extreme rainfall based on weather patterns subsampling

Evin, Guillaume; Blanchet, Juliette; Paquet, Emmanuel; Garavaglia, Federico; Penot, David

doi:10.1016/j.jhydrol.2016.08.024

Cited by 33 publications

(47 citation statements)

References 38 publications

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“…This is performed using a simple technique based on a decorrelation distance. Evin et al (2016) decided not to use such a method because it reduces the sample size. Better performance is expected using recently proposed statistical models (Buishand et al, 2008;Davison et al, 2012).…”

Section: Prospectsmentioning

confidence: 99%

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Goudenhoofdt

Delobbe

Willems

2017

Hydrol. Earth Syst. Sci.

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Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005-2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965-2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

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Section: Prospectsmentioning

confidence: 99%

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Goudenhoofdt

Delobbe

Willems

2017

Hydrol. Earth Syst. Sci.

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“…8) using a regionalization method similar to that of Evin et al (2016), which can be summarized as follows.…”

Section: Intensity Processmentioning

confidence: 99%

“…-the determination of robust estimates of the shape parameter of this distribution, which indicates the heaviness of the tail, using a regionalization approach, as in Evin et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Stochastic generation of multi-site daily precipitation focusing on extreme events

Evin

Favre

Hingray

2018

Hydrol. Earth Syst. Sci.

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Abstract. Many multi-site stochastic models have been proposed for the generation of daily precipitation, but they generally focus on the reproduction of low to high precipitation amounts at the stations concerned. This paper proposes significant extensions to the multi-site daily precipitation model introduced by Wilks, with the aim of reproducing the statistical features of extremely rare events (in terms of frequency and magnitude) at different temporal and spatial scales. In particular, the first extended version integrates heavy-tailed distributions, spatial tail dependence, and temporal dependence in order to obtain a robust and appropriate representation of the most extreme precipitation fields. A second version enhances the first version using a disaggregation method. The performance of these models is compared at different temporal and spatial scales on a large region covering approximately half of Switzerland. While daily extremes are adequately reproduced at the stations by all models, including the benchmark Wilks version, extreme precipitation amounts at larger temporal scales (e.g., 3-day amounts) are clearly underestimated when temporal dependence is ignored.

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“…The geographical distance itself might also be improved, e.g. by better accounting for the terrain characteristics (Gottardi et al, 2012;Evin et al, 2016) or by considering statistical distance (Ahrens, 2006). Also more robust estimates of the marginal parameters at station locations (i.e.…”

mentioning

confidence: 99%

“…Such idea has been quite widely used in the context of rainfall extremes (e.g. Carreau et al, 2013;Evin et al, 2016, for the studied region). However we anticipate the gain to be much less pregnant when interest is in modeling any rainfall -as in this study-, and not only the extreme ones since parameter estimation is already based on many data (several thousands).…”

mentioning

confidence: 99%

An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

Blanchet

Paquet²,

Ayar

et al. 2018

Preprint

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Abstract. We propose an objective framework for estimating rainfall cumulative distribution function within a region when data are only available at rain gauges. Our methodology is based on the evaluation of several goodness-of-fit scores in a crossvalidation framework, allowing to assess goodness-of-fit of the full distribution but with a particular focus on its tail. Crossvalidation is applied both to select the most appropriate statistical distribution at station locations and to validate the mapping of these distributions. Our methodology is applied to daily rainfall in the Ardèche catchment in South of France, a 2260 km 2 5 catchment with strong disparities in rainfall distribution. Results show preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with thin plate spline across this region. However the framework presented in this paper is general and could be likewise applied in any region, with possibly different conclusion depending on the subsequent rainfall processes.

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A regional model for extreme rainfall based on weather patterns subsampling

Cited by 33 publications

References 38 publications

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Stochastic generation of multi-site daily precipitation focusing on extreme events

An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

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