A spatial and nonstationary model for the frequency of extreme rainfall events

Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan; Arnbjerg‐Nielsen, Karsten

doi:10.1029/2012wr012570

Cited by 37 publications

(43 citation statements)

References 45 publications

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“…In the analysis of extreme precipitation in Belgium, Ntegeka and Willems (2008) found multi-decadal oscillations that can be partly explained by atmospheric circulation patterns, and a departure from this oscillation with trends towards more extreme precipitation in recent decades may be an indication of climate change. Gregersen et al (2013b) showed that the temporal variations in the frequency of extreme rainfall events in Denmark can be partly explained by atmospheric circulation patterns, average summer precipitation, and average summer temperature.…”

Section: Key Findingsmentioning

confidence: 99%

Review of trend analysis and climate change projections of extreme precipitation and floods in Europe

Madsen¹,

Lawrence²,

Lang³

et al. 2014

Journal of Hydrology

553

318

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Please cite this article as: Madsen, H., Lawrence, D., Lang, M., Martinkova, M., Kjeldsen, T.R., Review of trend analysis and climate change projections of extreme precipitation and floods in Abstract This paper presents a review of trend analysis of extreme precipitation and hydrological floods in Europe based on observations and future climate projections. The review summaries methods and methodologies applied and key findings from a large number of studies. Reported analyses of observed extreme precipitation and flood records show that there is some evidence of a general increase in extreme precipitation, whereas there are no clear indications of significant trends at large-scale regional or national level of extreme streamflow. Several studies from regions dominated by snowmelt-induced peak flows report decreases in extreme streamflow and earlier spring snowmelt peak flows, likely caused by increasing temperature. The review of likely future changes based on climate projections indicates a general increase in extreme precipitation under a future climate, which is consistent with the observed trends. Hydrological projections of peak flows show large impacts in many areas with both positive and negative changes. A general decrease in flood magnitude and earlier spring floods are projected for catchments with snowmelt-dominated peak flows, which is consistent with the observed trends.Finally, existing guidelines in Europe on design flood and design rainfall estimation are reviewed. The review shows that only few countries have developed guidelines that incorporate a consideration of climate change impacts.

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Section: Key Findingsmentioning

confidence: 99%

Review of trend analysis and climate change projections of extreme precipitation and floods in Europe

Madsen¹,

Lawrence²,

Lang³

et al. 2014

Journal of Hydrology

553

318

View full text Add to dashboard Cite

show abstract

“…Willems (2013) concluded that a higher degree of explanation is obtained if the index is estimated from SLPD between Gibraltar and Scandinavia. Gregersen et al (2013a) found a correlation between the frequency of heavy rainfall events and the East Atlantic (EA) pattern, which is the second most prominent mode of SLP variability over the North Atlantic (NOAA 2013b).…”

Section: Introductionmentioning

confidence: 99%

“…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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“…However, no spatial explanatory variable was used. On the other hand, Gregersen et al [24] used regional spatial explanatory variables to handle spatial correlation in addition to using large-scale indices to explain temporal variability of the frequencies of rainfall extremes over Denmark and estimated the contribution of each explanatory variables (covariates) toward explaining spatial or temporal variation using a unregularized multivariate regression technique. While we borrow their approach of using regional-scale spatial variables to explain the spatial variability of the rainfall frequencies, our goal is to use as many variables as possible-both atmospheric variables at multiple spatial scales and large-scale climate indicesto use as potential covariates to explain any possible variability in extreme frequencies.…”

mentioning

confidence: 99%

A Bayesian Sparse Generalized Linear Model With an Application to Multiscale Covariate Discovery for Observed Rainfall Extremes Over the United States

Das

Ganguly

Obradović

2015

IEEE Trans. Geosci. Remote Sensing

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Predictive insights on extreme and rare events are important across multiple disciplines ranging from hydrology, climate, and remote sensing to finance and security. Characterizing the dependence of extremes on covariates can help in identification of plausible causal drivers and may even inform predictive modeling. However, despite progress in the incorporation of covariates in the statistical theory of extremes and in sparse covariate discovery algorithms, progress has been limited for high-dimensional data where the number of covariates is large. In this paper, we propose a general-purpose sparse Bayesian framework for covariate discovery based on a Poisson description of extremes frequency and a hierarchical Bayesian description of a sparse regression model. We obtain posteriors over regression coefficients, which indicate dependence of extremes on the corresponding covariates, using a variational Bayes approximation. Experiments with synthetic data demonstrate the ability of the approach to accurately characterize dependence structures. The method is applied to discover the covariates affecting the frequency of precipitation extremes obtained from station-level observations over nine climatologically homogeneous regions within the continental U.S. The candidate covariates at multiple spatial scales represent station-level as well as regional and seasonal atmospheric condition, indices that attempt to capture large-scale ocean-based climate oscillators and hence natural climate variability, as well as global warming. Our results confirm the dependence structures that may be expected from known precipitation physics and generate novel insights, which can inform physical understanding and perhaps even predictive modeling.Index Terms-Bayesian model, climate change, covariate discovery, sparse regression, statistical downscaling.

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A spatial and nonstationary model for the frequency of extreme rainfall events

Cited by 37 publications

References 45 publications

Review of trend analysis and climate change projections of extreme precipitation and floods in Europe

Review of trend analysis and climate change projections of extreme precipitation and floods in Europe

Long term variations of extreme rainfall in Denmark and southern Sweden

A Bayesian Sparse Generalized Linear Model With an Application to Multiscale Covariate Discovery for Observed Rainfall Extremes Over the United States

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